CLOSED-LOOP DRUG INFUSION SYSTEM WITH SYNERGIC CONTROL

Abstract

A system automatically calculates drug infusion applicable to a patient to induce a satisfactory anesthetic state during surgery. The automatic calculation system would be determined by target values of physiological monitors to evaluate the patient's condition. Automatic infusion increases patient safety, reducing post-surgical morbidity and mortality, and reduces continuous decision-making by the specialist. An electronic system implements a MIMO-PID controller that calculates the infusion of various drugs on the basis of a control error defined by deviations in the patient's condition, which is analyzed by several monitors. The automatic drug infusions are determined by safety systems for preventing under and/or over infusion events and are complemented with correction and feedback systems.

Claims

1. A system for the intravenous automatic multi-infusion of drugs with synergy to patients, the system comprising: an infusion pump subsystem, configured to deliver to a patient a number of drugs; a monitoring subsystem, configured to measure a set of physiological variables with patient status information; a control subsystem configured to adapt the amount delivered of each drug by the infusion pump subsystem, based on a predetermined initial infusion amount, monitoring target values, a feedback of the measured physiological variables and a synergy between the drugs, wherein the control subsystem comprises: a control error generating module configured to calculate errors based on the monitoring target values and the feedback of the measured physiological variables; a controller configured to determine a control infusion for each of the drugs based on the errors calculated by the control error generating module and the predetermined initial infusion amount; a correction module configured to receive the measurements of the set of physiological variables from the monitoring subsystem and modify the control infusion of the controller, increasing said infusion as a function of an upper threshold or decreasing it as a function of a lower threshold, to set the physiological variables at a preset safe range for the physiological variables; and a safety module configured to receive the control infusion of each drug and modify said infusion by limiting it between two infusion values, a lower limit and an upper limit, which ensure there is no over-medication for each drug.

2. The system according to claim 1 wherein the control error generating module is configured to generate, for each of the variables of the set of physiological variables with patient status information to be measured by the monitoring subsystem, a control vector and wherein the controller is of the MIMO-type multivariable character and comprises a MISO control subsystem for each drug, which in turn comprises SISO controllers configured to receive the control errors and determine a control infusion vector for each drug.

3. The system according to claim 1 further comprising: a quantification module connected between the safety module and the infusion pump subsystem, wherein the quantification module is configured to adapt the output vector of the safety module so as to be interpretable by the infusion pumps.

4. The system according to claim 1, wherein the control subsystem further comprises: a filter bank configured to receive the feedback signals sent by the monitoring subsystem.

5. The system according to claim 1, wherein the control error generating module comprises several error generating sub-modules for each of the variables of the set of variables monitored with patient status information to be measured by the monitoring subsystem.

6. The system according to claim 1, wherein the drugs are anesthetic drugs that induce the patient to an anesthetic state and wherein the set of physiological variables that measures the monitoring subsystem have information on said anesthetic state of the patient.

7. The system of claim 6, wherein the set of variables with patient anesthetic status information comprises eBIS, eNOX, and eNMB or equivalent monitors of hypnosis, nociception, or muscle relaxation, respectively.

8. The system according to claim 7 wherein the infusion pump subsystem comprises: a first infusion pump of a drug with hypnotic properties, a second infusion pump of a drug with analgesic properties and a third infusion pump of a drug with muscle relaxation properties.

9. The system according to claim 8 wherein the drug with hypnotic properties is propofol, the drug with analgesic properties is remifentanil and the drug with muscle relaxation properties is rocuronium.

10. The system according to claim 8, further comprising additional pumps for the infusion of other drugs that alter the anesthetic state or vital signs of the patient.

11. The system according to claim 1, wherein the control subsystem is customized specifically for the patient based on methods of tuning the controller through the various gains relative to each SISO controller and of at least one of the following physiological parameters specific to the patient: weight, height, sex, muscle mass and clinical history.

12. The system according to claim 2 wherein the MIMO controller is a MIMO-PID multivariable controller, the MISO control subsystems are MISO-PID type control subsystems, and the SISO controllers are of the SISO-PID type.

13. The system according to claim 12 wherein the control vector of the control error generating module, has a proportional asymmetric error component, an integral symmetric error component, a derivative asymmetric error component and additional error components, and wherein the SISO-PID controllers are configured to respectively receive the proportional asymmetric error component, the integral symmetric error component and the derivative asymmetric error component of each control vector and determine the control infusion vector for each drug.

14. The system according to claim 1 wherein the control subsystem is further configured for glucose control of type 1 diabetes patients, wherein the monitoring subsystem is further configured for measuring the glucose of the patient, and wherein the infusion subsystem is further configured for delivering to the patient a continuous and controlled amount of insulin and glucagon.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0162] FIG. 1 shows the topology of the SynPlD control system (24) in its most general form, showing the patient (1) to which the anesthetic drug infusion vector (5) is supplied through an infusion pump system (2) and from which the measurements of the monitoring variables vector (6) are gathered by the monitoring equipment (3). The infusion pumps control the drugs ePPF (2.1), eRMF (2.2) and eRCN (2.3). The monitoring equipment (3) collects information on the following variables: 1) DoH (3.1) via the eBIS monitor (6.1); 2) ANG (3.2) via the eNOX monitor (6.2); and 3) MRX (3.3) via the eNMB monitor (6.3). The monitoring variables vector (6) is the feedback base of the SynPlD control system (24), which also uses information on: 1) the monitored variables target vector (8): eBIS (8.1), eNOX target (8.2) and eNMB target (8.3); and 2) the manual protocol infusion vector (4): ePPF manual protocol (4.1), eRMF manual protocol (4.2) and eRCN manual protocol (4.3).

[0163] FIG. 2 shows a detailed view of the SynPlD control system (24) that has as inputs: 1) the manual protocol infusion vector (4); 2) the monitored variables target vector (8); and 3) the values of the monitoring variables vector (6). The SynPlD control system (24) is composed of: 1) a filter bank (9) for obtaining a filtered monitoring variables vector (10) from the monitoring variables vector (6); the filter bank (9) is composed of three filters, one for cleaning and filtering the eBIS (9.1), another for cleaning and filtering the eNOX (9.2) and another for cleaning and filtering the eNMB (9.3); 2) a control error generator (11) for obtaining the control error vectors (12, 13, 14) from the filtered monitoring variable vector (10) and the target monitored variables vector (8); specifically, this consists of obtaining the eBIS monitor control error vector (12); obtaining the eNOX monitor control error vector (13) and obtaining the eNMB monitor control error vector (14); 3) a multivariable and synergistic MIMO-PID controller (16) to obtain a control infusion vector (15) from the control error vectors (12, 13, 14) and from the manual protocol infusion vector (4); 4) an infusion correcting system (17) for obtaining a correction vector (18) from the filtered monitored variables vector (10); 5) a safety system (19) for obtaining a reliable control vector (20) from the control infusion vector (15) and the correction vector (18); 6) a quantifier (21) for obtaining an automatic infusion vector (22) from the reliable control vector (20); and 7) a switching system (23) for obtaining the anesthetic drug infusion vector (5) from the manual protocol infusion vector (4) and the automatic infusion vector (22).

[0164] FIG. 3 shows a detailed view of the control error generator (11) which has as inputs: 1) the monitored variables target vector (8); and 2) the filtered monitoring variables vector (10). The control error generator (11) is composed of: 1) a DoH error generator (28) from where the eBIS monitor control error vector (12) is obtained, where the proportional error eBIS (12.1), the integral error eBIS (12.2) and the derivative error eBIS (12.3) are indicated, based on the filtered monitoring of the DoH by the eBIS monitor (10.1) and its target value (8.1); 2) an ANG error generator (29) where the eNOX monitor control error vector (13) is obtained, wherein the eNOX proportional error (13.1), the eNOX integral error (13.2) and the eNOX derivative error (13.3) are indicated, from the filtered monitoring of the ANG by the eNOX monitor (10.2) and its target value (8.2); and 3) an MRX error generator (30) where the eNMB monitor control error vector (14) is obtained, wherein the eNMB proportional error (14.1), the eNMB integral error (14.2) and the eNMB derivative error (14.3) are indicated, based on the filtered monitoring of the MRX using the eNMB monitor (10.3) and its target value (8.3).

[0165] FIG. 4 shows a detailed view of the multivariable controller with MIMO-PID synergy (16) that has as inputs the control error vectors (12, 13, 14) and the manual protocol infusion vector (4). The MIMO-PID multivariable and synergistic controller (16) is composed of miscellaneous MISO-PID control subsystems (25, 26, 27) that relate the control error vectors (12, 13, 14) to the control infusion vector (15).

[0166] FIG. 5 shows a detailed view of the MISO-PID controller (25) related to the infusion of the ePPF drug (15.1) that has as inputs the control error vectors (12, 13, 14). The MISO-PID-ePPF control subsystem (25) is composed of three SISO-PID controllers that relate the infusion of the ePPF drug with: 1) the eBIS control error vector (12) via an eBIS-ePPF PID controller (25.1); 2) the eNOX control error vector (13) via an eNOX-ePPF PID controller (25.2); and 3) the eNMB control error vector (14) via an eNMB-ePPF PID controller (25.3).

[0167] FIG. 6 shows a detailed view of the MISO-PID-eRMF control subsystem (26) related to the infusion of the eRMF drug (15.2) that has as inputs the control error vectors (12, 13, 14). The MISO-PID-eRMF control subsystem (26) is composed of three SISO-PID controllers that relate the infusion of the eRMF drug with: 1) the eBIS control error vector (12) via an eBIS-eRMF PID controller (26.1); 2) the eNOX control error vector (13) via an eNOX-eRMF PID controller (26.2); and 3) the eNMB control error vector (14) via an eNMB-ePPF PID controller (26.3).

[0168] FIG. 7 shows a detailed view of the MISO-PID-eNMB control subsystem (27) related to the infusion of the eRCN drug (15.3) that has as inputs the control error vectors (12, 13, 14). The MISO-PID-eNMB control subsystem (27) is composed of three SISO-PID controllers that relate the infusion of the eRCN drug with: 1) the eBIS control error vector (12) via an eBIS-eRCN PID controller (27.1); 2) the eNOX control error vector (13) via an eNOX-eRCN PID controller (27.2); and 3) the eNMB control error vector (14) via an eNMB-eRCN PID controller (27.3).

[0169] FIG. 8 shows a detailed view of one of the 9 SISO-PID controllers (Cu) that make up the SynPlD control system (24); each SISO-PID controller has as inputs the corresponding proportional (k.1), integral (k.2) and derivative (k.3) errors, relative to each component of each control error vector (e.sub.j). Each SISO-PID controller is identified with the subscripts/that identifies the drug to which the partial infusion is being calculated, and j that identifies the origin of the monitor. Thus i=1 Identifies the PPF, i=2 the RMF and i=3 the RCN, j=1 Identifies the monitor BIS, j=2 the NOX and j=3 the NMB, k=12 Identifies the error vector relative to the monitor BIS, k=13 the one relative to the NOX and k=14 the NMB.

DETAILED DESCRIPTION OF THE INVENTION

[0170] The present invention provides to the art various strategies of complementing the PID controller in CL to be effective in the control in CL of the Induction and maintenance of the general anesthesia, calculating the dose of the ePPF, eRMF and eRCN drugs based on a control error vector. Control in CL is obtained in a novel way by considering the synergy of the drugs in the act of anesthesia and by the feedback of the monitoring information of DoH, ANG and MRX (other variables can be considered) through the eBIS, eNOX and eNMB monitors respectively (other monitors can be considered). The changes in the values of the monitors are mainly caused by the effect of drugs and the acts of surgery.

[0171] In addition, drug infusions are conditioned by a safety system to safeguard the physical integrity of the patient and are complemented by an infusion correction system that the classic PID does not have. Among its novelties, it presents the consideration of the synergy between drugs in a PID multi-controller, asymmetry in control errors, correction of infusion for exceptionally low or high levels in monitors, safety against overinfusion through fixed or variable limits of drug infusion and by suspension of pumps, safety against underinfusion due to conditions related to the manual experience of the anesthesiologist (OL manual protocol). Finally, the final decision on the infusions is conditioned with a quantifier to adapt the infusions to the resolutions of the electromechanical infusion pumps.

[0172] The final objective is to perform an automatic calculation of the infusions of ePPF, eRMF and eRCN that would be applied directly to a patient under surgery under general anesthesia, who would be the end user of the present invention. The proposed invention has among its novelties the definition of a new anesthetic drug infusion system with a SynPlD control system, formed by a filter bank, an asymmetric control error generator, a MIMO-PID controller, a correction system, a safety system and a quantification system that can be applied to the patient under surgery, since it takes into account: 1) monitoring of the DoH, ANG and MRX using eBIS, eNOX and eNMB monitors; 2) the infusion pumps of the ePPF, eRMF and eRCN drugs; and 3) the use of an electronic device that uses a microprocessor and a storage memory.

[0173] In addition, to estimate the automatic infusions on the patient, the CL control system considers the previous drug infusions at the time of action and considers the measurements of current and past monitors.

[0174] The continuous infusion system of ePPF, eRMF and eRCN drugs with SynPlD control system is a control method that requires the manual experience of the anesthesiologist (OL manual protocol) and is customized for each patient using a variant of the Cohen-Coon empirical adjustment method using the proportional, integral and derivative gains of each SISO-PID controller, the patient's weight (W), height (H), sex (G) and muscle mass (MB). The customization method is applied during the IPh of general anesthesia.

[0175] The SynPlD control system (24) proposes the anesthetic drug infusion vector (5), as a result of the application of the switching system (23), which initially applies the manual protocol infusion vector (4) in the IPh, to switch the automatic infusion vector (22) in MPh and override the infusion in RPh.

[0176] The main objective of the SynPlD control system (24) is to take the patient (1) to a satisfactory and reliable anesthesia state based on: 1) the current anesthesia state; 2) the vector of current and/or past monitoring variables (6) (history stored in memory); and 3) the vector of infusion of anesthetic drugs (5) of past times (history stored in memory). The invention consists in the proposal of an electronic system in which a feedback control algorithm is executed defined by a SynPlD control system (24) based on a multivariable controller and with MIMO-PID synergy (16) that defines the multivariable control system to perform the control of a reliable anesthetic state in patients (1) undergoing surgery under general anesthesia.

[0177] The SynPlD control system is implemented in a microprocessor-based system with memory selected from: smartphones, tablets, personal computers, arduino, raspberry-PI and specific hardware for the execution of the method or use the hardware of the infusion pumps to execute the sequence of instructions that would implement the drug infusion method in a SynPlD control system described in the present invention.

[0178] The synergistic drug infusion system by control in CL MIMO-PID (SynPlD) applied in IV anesthetic acts of the present invention differs from the prior art methods based on classical PIDs in at least the following aspects: [0179] The SynPlD control system uses three control phases controlled by the switching system (23): 1) IPh, the phase corresponding to the beginning of the anesthetic act and lasts until a satisfactory anesthetic state is achieved; during this period only the manual protocol infusion vector (4) is applied to the patient (1), while the end of this period is used for the customization of the SynPlD control system (24) to the patient (1); 2) MPh, in this phase it is where the surgery takes place and the objective of the SynPlD control system is to maintain the satisfactory anesthetic state, despite the surgical acts; and 3) RPh, in this phase the infusions of the drugs are removed to achieve patient awareness. [0180] The SynPlD control system uses a filter bank (9) to remove noise and artifacts from the monitoring variable vector (6) and obtain the filtered monitoring vector. The filter bank (9) is based on spectral cleaning and decision-making based on monitoring conditions. [0181] The SynPlD control system uses a control error generator (11) relating to the eBIS, eNOX and eNMB monitors that make up the monitoring variable vector (6); the errors are asymmetric and are defined by a modulation of the target vector of the monitored variables (8) and the filtered monitoring variable vector (10). The vector of surgical actions (7) is reflected in the patient (1) through variations in the monitoring variables vector (6). [0182] The SynPlD control system uses a control infusion vector (15) formed by infusions of the drugs ePPF (15.1), eRMF (15.2) and eRCN (15.3). Each drug infusion is defined by the action of a MISO-PID control system composed of the sum of three control actions relating to three SISO-PID controllers. [0183] The infusion of ePPF (15.1) is calculated by the sum of: 1) a SISO-PID controller (25.1) that relates a portion of the ePPF control infusion to the eBIS monitor control error vector (12), formed by a proportional component (12.1), an integral component (12.2), and a derivative component (12.3); 2) a SISO-PID controller (25.2) that relates a portion of the ePPF control infusion to the eNOX monitor control error vector (13), formed by a proportional component (13.1), an integral component (13.2), and a derivative component (13.3); and 3) a SISO-PID controller (25.3) that relates a portion of the ePPF control infusion to the eNMB monitor control error vector (14), formed by a proportional component (14.1), an integral component (14.2), and a derivative component (14.3); [0184] The infusion of eRMF (15.2) is calculated by the sum of: 1) a SISO-PID controller (26.1) that relates a portion of the control infusion of the eRMF to the control error vector of the eBIS monitor (12), formed by a proportional component (12.1), an integral component (12.2) and a derivative component (12.3); 2) a SISO-PID controller (26.2) that relates a portion of the control infusion of the eRMF to the control error vector of the eNOX monitor (13), formed by a proportional component (13.1), an integral component (13.2) and a derivative component (13.3); and 3) a SISO-PID controller (26.3) that relates a portion of the control infusion of the eRMF to the control error vector of the eNMB monitor (14), formed by a proportional component (14.1), an integral component (14.2) and a derivative component (14.3); [0185] The infusion of eRCN (15.3) is calculated by the sum of: 1) a SISO-PID controller (27.1) that relates a portion of the eRCN control infusion to the eBIS monitor control error vector (12), formed by a proportional component (12.1), an integral component (12.2), and a derivative component (12.3); 2) a SISO-PID controller (27.2) that relates a portion of the eRCN control infusion to the eNOX monitor control error vector (13), formed by a proportional component (13.1), an integral component (13.2), and a derivative component (13.3); and 3) a SISO-PID controller (27.3) that relates a portion of the eRCN control infusion to the eNMB monitor control error vector (14), formed by a proportional component (14.1), an integral component (14.2), and a derivative component (14.3); [0186] The SynPlD control system (24) uses 9 SISO-PID controllers, grouped into three MISO-PID control subsystems (FIGS. 5, 6 and 7), each SISO-PID controller (C) (FIG. 8) being used to calculate drug infusion i (15.i): [0187] A proportional action system (i.j.1) that defines at its output the proportional infusion action (15.i.1), starting from its input which is the proportional asymmetric control error (k.1). [0188] An integral action system (i.j.2) that defines at its output the integral infusion action (15.i.2), starting from its input which is the integral symmetric control error of (k.2). [0189] A derivative action system (i.j.3) that defines at its output the derivative infusion action (15.i.3), from its input which is the asymmetric derivative control error (k.3). [0190] The SynPlD control system (24) uses an infusion correction system (17), which compensates the control infusion vector (15) with the correction vector (18) which are defined by the deviations of the vector of filtered monitoring variables (10) with respect to some vectors of upper and lower thresholds. These deviations could not be corrected by the multivariable control based on PIDs (24). [0191] The SynPlD control system (24) uses a safety system (19), which conditions the control infusion vector (15) compensated by the correction vector (18) to lead to the reliable control vector (20), which prevents overinfusion and underinfusion, thereby avoiding periods of deep general anesthesia that compromise the patient's life or periods of sedation which can cause intraoperative awakenings. To avoid overinfusion, restrictions are applied with the safety system (19) to suspend the infusion pump system (2) and to avoid underinfusion, the correction vector (18) is applied. [0192] The SynPlD control system (24) uses a quantification system (21) so that the reliable control vector (20) is interpretable by the infusion pump system (2) which, through various catheters, infuse the drugs intravenously in the patient's body (1) modifying the anesthetic state; subsequently, these modifications will be collected by the monitoring equipment (3) by means of the monitoring variable vector (6).

[0193] The steps for the multi-infusion of anesthetic drugs with synergy by control in CL MIMO-PID (SynPlD) applied in anesthetic acts via IV for automatic infusion, executable in an electronic device, are the following for each moment of action: [0194] Step 1: Measuring the value of the vector of the eBIS (6.1), eNOX (6.2) and eNMB (6.3) monitors, which mainly depends on the infusion vector of the anesthetic ePPF (5.1), eRMF (5.2) and eRCN (5.3) drugs. Anesthetic drugs are administered to the patient (1) by means of an infusion pump system (2). The monitoring equipment (3) collects the effects of the anesthetic drugs on the patient and the surgical actions vector (7) those of the surgeon in a procedure through the monitoring variables vector (6); other factors can influence the value of the monitoring vector. The information of the monitoring variables vector (6) and the decision made on the value of the infusion vector of anesthetic drugs (5) is stored in a memory; [0195] Step 2: Calculating the infusion vector of the anesthetic ePPF (5.1), eRMF (5.2) and eRCN (5.3) drugs by means of a SynPlD control system (24) from the feedback of the vector value of the eBIS (6.1), eNOX (6.2) and eNMB (6.3) monitors, of the manual protocol infusion vector relating to the ePPF (4.1), to the eRMF (4.2) and to the eRCN (4.3) and of the vector of the target values of monitored variables relating to eBIS (8.1), to eNOX (8.2) and to eNMB (8.3); [0196] Step 3: Proceed with the cleaning of noise and artifacts of the vector of the eBIS (6.1), eNOX (6.2) and eNMB (6.3) monitors, by means of a cleaning and filtering system for the eBIS (9.1), a cleaning and filtering system for the eNOX (9.2) and a cleaning and filtering system for the eNMB (9.3), to obtain a filtered monitoring variables vector of the eBIS (10.1), eNOX (10.2) and eNMB (10.3); [0197] Step 4: Calculate control error vectors of the monitors to the eBIS (12), eNOX (13) and eNMB (14). The eBIS control error vector has three components: an eBIS proportional asymmetric error (12.1), an eBIS integral symmetric error (12.2), and an eBIS derivative asymmetric error (12.3). The eNOX control error vector has three components: an eNOX proportional asymmetric error (13.1), an eNOX integral symmetric error (13.2), and an eNOX derivative asymmetric error (13.3). The eNMB control error vector has three components: an eNMB proportional asymmetric error (14.1), an eNMB integral symmetric error (14.2), and an eNMB derivative asymmetric error (14.3). The control error vectors (12, 13, 14) are obtained by a control error generator (11) composed of a DoH error generator (28), an ANG error generator (29) and an MRX error generator (30). The DoH error generator (28) obtains the three components, eBIS proportional asymmetric error (12.1), integral symmetric error (12.2) and derivative asymmetric error (12.3), from the filtered monitoring variable of the eBIS (10.1) and the target value of the eBIS (8.1). The ANG error generator (29) obtains the three components, eNOX proportional asymmetric error (13.1), integral symmetric eNOX (13.2) and derivative asymmetric eNOX (13.3) from the filtered monitoring variable of the eNOX (10.2) and the target value of the eNOX (8.2). The MRX error generator (30) obtains the three components eNMB proportional asymmetric error (14.1), eNMB integral symmetric (14.2) and eNMB derivative asymmetric (14.3) from the filtered monitoring variable of the eNMB (10.3) and the target value of the eNMB (8.3). The errors will indicate at the time of their evaluation the deviation of the monitoring variables vector (6) with respect to the vector of objectives of the monitored variables (8). [0198] Step 5: Calculating a drug control infusion vector for ePPF (15.1), eRMF (15.2), and eRCN (15.3) by means of a MIMO-PID synergistic and multi-variable controller (16) that inputs the control error vectors (12, 13, 14) and the manual protocol infusion vector (4). The MIMO-PID multivariable and synergistic controller (16) consists of three MISO-PID control subsystems (25, 26, 27), which in turn each consists of three SISO-PID controllers: 1) SISO-PID controllers (25.1, 25.2, 25.3), used to obtain the ePPF control infusion (15.1); 2) SISO-PID controllers (26.1, 26.2, 26.3), used to obtain the eRMF control infusion (15.2); and 3) SISO-PID controllers (27.1,27.2, 27.3), used to obtain the eRCN control infusion (15.3). [0199] Step 6: Calculating a correction vector (18) formed by the ePPF corrective infusion (18.1), the eRMF corrective infusion (18.2) and the eRCN corrective infusion (18.3) by an infusion correction system (17) which is formed by an ePPF infusion correction subsystem (17.1), an eRMF infusion correction subsystem (17.2) and an eRCN infusion correction subsystem (17.3) from the filtered monitoring variable vector (10) composed of the eBIS filtered monitoring (10.1), the eNOX filtered monitoring (10.2) and the eNMB filtered monitoring (10.3); the correction vector (18) attempts to compensate the control infusion vector (15) in order to avoid underinfusion to mitigate the effect of the decrease in DoH, ANG and MRX; the correction vector (18) is calculated on the basis of an upper and lower thresholds vector on the filtered variable vector (10); [0200] Step 7: Calculating a reliable control vector (20) formed by the reliable infusion of ePPF (20.1), the reliable infusion of eRMF (20.2) and the reliable infusion of eRCN (20.3) by a safety system (19) which is formed by an ePPF infusion safety subsystem (19.1), an eRMF infusion safety subsystem (19.2) and an eRCN infusion safety subsystem (19.3) from the control infusion vector (15) formed by the ePPF control infusion (15.1), the control infusion of the eRMF (15.2) and the control infusion of the eRCN (15.3) and from the correction vector (18) formed by the corrective infusion of ePPF (18.1), the corrective infusion of eRMF (18.2) and the corrective infusion of eRCN (18.3); the safety system (19) attempts to compensate the control infusion vector (15) to drive the reliable control vector (20) to a reliable value for the patient (1) and reliable to cause the desired effect on the patient (1), which prevents overinfusion and underinfusion, thereby avoiding periods of deep general anesthesia that compromises the patient's life or periods of sedation which can cause intraoperative awakenings; to avoid overinfusion, suspension restriction vectors of the infusion pump system are applied (2) and to avoid underinfusion, the correction vector (18) is applied; [0201] Step 8: Calculating an automatic infusion vector (22) formed by the automatic infusion of ePPF (22.1), the automatic infusion of eRMF (22.2) and the automatic infusion of eRCN (22.3) by a quantification system (21) which is formed by an infusion quantification subsystem of the ePPF (21.1), an infusion quantification subsystem of the eRMF (21.2) and an infusion quantification subsystem of the eRCN (21.3) from a reliable control vector (20) formed by the reliable infusion of ePPF (20.1), the reliable infusion of eRMF (20.2) and the reliable infusion of eRCN (20.3); the quantification system (21) modifies the infusions so that the reliable control vector (20) is interpretable by the infusion pump system (2), composed of the ePF pump (2.1), the eRMF pump (2.2) and the eRCN pump (2.3), which infuse the drug through various IV catheters to the patient (1); [0202] Step 9: Defining the anesthetic drug infusion vector (5) composed of the ePPF infusion (5.1), the eRMF infusion (5.2) and the eRCN infusion (5.3) by a switching system (23) to control the time elapsed in IPh, the time in MPh and the time in RPh; in IPh the anesthetic drug infusion vector (5) is the same as the manual protocol infusion vector (4); in MPh the anesthetic drug infusion vector (5) is the same as the automatic infusion vector (22); and in RPh the suspension of the infusion pump system (2) is activated; [0203] Step 10: Calculating the control infusion of the ePPF drug (15.1) from the MISO-PID-ePPF C.sub.1 control subsystem (25), which in turn is formed by three SISO-PID controllers (C.sub.11, C.sub.12, C.sub.13) (25.1, 25.2, 25.3), each SISO-PID controller in turn formed by three drug control actions that are detailed below: 1) the SISO-PID CD controller (25.1) that calculates the contribution to the infusion of the ePPF drug infusion (15.1.1) provided by the eBIS monitor control error vector (12), is formed by a proportional action that relates the partial infusion of ePPF (15.1.1.1) to the proportional control asymmetric error relative to the eBIS (12.1), by an integral action that relates the partial infusion of ePPF (15.1.1.2) to the integral control symmetric error relative to the eBIS (12.2) and by a derivative action that relates the partial infusion of ePPF (15.1.1.3) to the derivative asymmetric error relative to eBIS (12.3); 2) the SISO-PID C.sub.72 controller (25.2) that calculates the contribution to the control infusion of the ePPF drug infusion (15.1.2) provided by the control error vector of the eNOX monitor (13) is formed by a proportional action that relates the partial infusion of ePPF (15.1.2.1) to the asymmetric error of proportional control relative to the eNOX (13.1), by an integral action that relates the partial infusion of ePPF (15.1.2.2) to the integral control symmetric error relative to eNOX (13.2) and by a derivative action that relates the partial infusion of ePPF (15.1.2.3) to the derivative asymmetric error relative to eNOX (13.3); 3) the SISO-PID C.sub.l 3 controller (25.3) that calculates the contribution to the control infusion of the ePPF drug infusion (15.1.3) provided by the control error vector of the eNMB monitor (14) is formed by a proportional action that relates the partial infusion of ePPF (15.1.3.1) to the proportional control asymmetric error relative to eNMB (14.1), by an integral action that relates the partial infusion of ePPF (15.1.3.2) to the integral control symmetric error relative to eNMB (14.2) and by a derivative action that relates the partial infusion of ePF (15.1.3.3) to the derivative asymmetric error relative to the eNMB (14.3); [0204] Step 11: Calculating the control infusion of the eRMF drug (15.2) from the MISO-PID-eRMF C.sub.2 control subsystem (26), which in turn is formed by three SISO-PID controllers (C.sub.21, C.sub.22, C.sub.23) (26.1, 26.2, 26.3), each SISO-PID controller in turn formed by three drug control actions that are detailed below: 1) the SISO-PID C.sub.21 controller (26.1) that calculates the contribution to the control infusion of the eRMF drug infusion (15.2.1) provided by the control error vector of the eBIS monitor (12), is formed by a proportional action that relates the partial infusion of ePPF (15.2.1.1) to the asymmetric error of proportional control relative to the eBIS (12.1), by an integral action that relates the partial infusion of eRMF (15.2.1.2) to the symmetric error of integral control relative to the eBIS (12.2) and by a derivative action that relates the partial infusion of eRMF (15.2.1.3) to the derivative asymmetric error relating to eBIS (12.3); 2) the SISO-PID C.sub.22 controller (26.2) that calculates the contribution to the control infusion of the eRMF drug infusion (15.2.2) provided by the control error vector of the eNOX monitor (13) is formed by a proportional action that relates the partial infusion of eRMF (15.2.2.1) to the asymmetric error of proportional control relating to eNOX (13.1), by an integral action that relates the partial infusion of eRMF (15.2.2.2) to the integral control symmetric error relating to eNOX (13.2) and by a derivative action that relates the partial infusion of eRMF (15.2.2.3) to the derivative asymmetric error relating to eNOX (13.3); 3) the SISO-PID C.sub.23 controller (26.3) that calculates the contribution to the control infusion of the eRMF drug infusion (15.2.3) provided by the eNMB monitor control error vector (14), is formed by a proportional action that relates the partial infusion of eRMF (15.2.3.1) to the proportional control asymmetric error relating to eNMB (14.1), by an integral action that relates the partial infusion of eRMF (15.2.3.2) to the integral control symmetric error relating to eNMB (14.2) and by a derivative action that relates the partial infusion of eRMF (15.2.3.3) to the derivative asymmetric error relating to eNMB (14.3); [0205] Step 12: Calculating the control infusion of the eRCN drug (15.3) from the MISO-PID-eNMB C.sub.3 control subsystem (27), which in turn is formed by three SISO-PID controllers (C.sub.31, C.sub.32, C.sub.33) (27.1, 27.2, 27.3), each SISO-PID controller is in turn formed by three drug control actions that are detailed below: 1) the SISO-PID C.sub.37 controller (27.1) that calculates the contribution to the control infusion of the eRCN drug infusion (15.3.1) provided by the control error vector relative to the eBIS (12) is formed by a proportional action that relates the partial infusion of eRCN (15.3.1.1) to the proportional control asymmetric error relative to the eBIS (12.1), by an integral action that relates the partial infusion of eRCN (15.3.1.2) to the integral control symmetric error relative to the eBIS (12.2) and by a derivative action that relates the partial infusion of eRCN (15.3.1.3) to the derivative asymmetric error relative to the eBIS (12.3); 2) the SISO-PID C.sub.32 controller (27.2) that calculates the contribution to the control infusion of the eRCN drug infusion (15.3.2) provided by the control error vector of the eNOX monitor (13), is formed by a proportional action that relates the partial infusion of eRCN (15.3.2.1) to the asymmetric error of proportional control relative to the eNOX (13.1), by an integral action that relates the partial infusion of eRCN (15.3.2.2) to the integral control symmetric error relative to the eNOX (13.2) and by a derivative action that relates the partial infusion of eRCN (15.3.2.3) to the derivative asymmetric error relative to the eNOX (13.3); 3) the SISO-PID C.sub.33 controller (27.3) that calculates the contribution to the control infusion of the eRCN drug infusion (15.3.3) provided by the control error vector relative to eNMB (14), is formed by a proportional action that relates the partial infusion of eRMF (15.3.3.1) to the proportional control asymmetric error relative to the eNMB (14.1), by an integral action that relates the partial infusion of eRCN (15.3.2) to the integral control symmetric error relative to the eNMB (14.2) and by a derivative action that relates the partial infusion of eRCN (15.3.3.3) to the derivative asymmetric error relative to the eNMB (14.3.3);

[0206] Next, the equations are expressed that define the SynPlD control system which, using the manual protocol infusion vector (4), the monitoring variables vector (6), the vector of objectives of the monitored variables (8), the vectors of control errors (12, 13, 14), the correction vector (18) and the reliable control vector (20), obtains an anesthetic drug infusion vector (5) that will keep the patient (1) in a satisfactory anesthetic state despite surgery. The drugs considered are ePPF, eRMF and eRCN and the monitors considered are eBIS, eNOX and eNMB, so the order of the vectors is fixed at 3×1 and the order of the matrices at 3×3, in this exemplary embodiment of the invention. It should be noted that the present invention does not make use of pharmacokinetic or pharmacodynamic models for its design or for personalization to each patient (1).

[0207] Before detailing the method and in order to clarify the variables that appear in the equations, the most important ones grouped by their physical units and common characteristics are listed below: [0208] 1) The dimensions of the multivariable system are 3×3, relative to the monitoring variables vector (6) r=3, where 1 refers to the eBIS, 2 refers to the eNOX and 3 refers to the eNMB; and relative to the anesthetic drug infusion vector (5) s=3, where 1 refers to the ePPF, 2 refers to the eRMF and 3 refers to the eRCN; [0209] 2) The discrete time is represented with the integer k, so that a real time t, can be shown as: t=k.Math.T.sub.s−, where T.sub.s is the infusion period; the IPh start time is t.sub.i, the MPh start time is t.sub.m and the RPh start time is t.sub.r. [0210] 3) Vectors that refer to drug infusions, infusion units 15 per patient weight [μg (min kg)], general equation:

u.sub.x[u.sub.x1 u.sub.x2 u.sub.x3].sup.T,

[0211] Where u.sub.x refers to the infusion column vector with dimension (3×1); u.sub.x1 is the infusion of the ePPF; u.sub.x2 is the infusion of the eRMF; u.sub.x3 is the infusion of the eRCN; and T indicates the transposition of the vector.

[0212] The list of infusion variables is as follows:

TABLE-US-00004 X Ref. Infusion Vector Symbol Eq. ol 4 Manual protocol (OL) u.sub.ol [2.2] pt 5 of anesthetic drugs u.sub.p pid 15 of drug control u.sub.pid [5.1] cr 18 drug corrector u.sub.cr [6.1] sf 20 reliable drug delivery u.sub.sf [7.1] Hl of upper limit u.sub.HI [7.3] LO of lower limit u.sub.LO [7.4] cl 22 automatic in CL u.sub.cl [8.1] mn resolution of pumps u.sub.mn [8.3] x: refers to the subscript of the vector considered; Ref. Indicates the number that appears in the figures; Symbol: Identifies the variable by its name; Eq. Indicates the equation of the general description where it has been defined. [0213] 4) Vectors that refer to the monitoring variables, monitoring units (UM) and the range of variation is from 0 to 100; general equation:

y.sub.z=[y.sub.z1y.sub.z2y.sub.z3].sup.T,

[0214] Where y.sub.z refers to the column vector monitoring variables (3×1); y.sub.z1 is eBIS monitoring; y.sub.z2 is eNOX monitoring; y.sub.z3 is eNMB monitoring; T indicates vector transposition.

[0215] The list of monitoring variables and their variants is as follows:

TABLE-US-00005 z Ref. Description of vector of Symbol Eq. 6 measured monitoring variables y [2.1] T 8 target values of the monitored variables y.sub.T [2.6] f 10 filtered monitoring variables y.sub.f [3.1] Hl upper thresholds for the activation of y.sub.HI [6.3] the correction LO lower thresholds for the activation of y.sub.LO [6.4] the correction z: refers to the subscript of the vector considered; Ref. indicates the number that appears in the figures; Symbol: Identifies the variable by its name; Eq. indicates the equation of the general description where it has been defined. [0216] 5) Vectors referring to the vectors of control errors (12, 13, 14), with variation range ±200 UM; general equation:

e.sub.j=[e.sub.Pje.sub.Ije.sub.Fj].sup.T, [0217] Where e.sub.1 refers to the control error column vector (3×1); e.sub.1 is the control error vector relative to the eBIS; e.sub.2 is the control error vector relative to the eNOX; 15 e.sub.3 is the control error vector relative to the eNMB; e.sub.Pi is the proportional control asymmetric error vector relative to the monitor y; e.sub.ii is the integral control symmetric error vector relative to the monitor y; and e.sub.Fi is the derivative control asymmetric and filtering error vector relative to the monitor y; T indicates the vector transposition.

[0218] The list of error variables and their variants is as follows:

TABLE-US-00006 j Ref. Description of the control error Symbol Eq. 1 12 eBIS vector e.sub.1 [4.1] 1 12.1 eBIS proportional asymmetric component e.sub.P1 [4.2] 1 12.2 eBIS integral symmetric component E.sub.I1 1 12.3 eBIS filtered derivative asymmetric e.sub.F1 component 2 13 eNOX vector e.sub.2 [4.1] 2 13.1 proportional asymmetric component e.sub.P1 [4.2] related to eNOX 2 13.2 integral symmetrical component relating E.sub.I1 to eNOX 2 13.3 filtered derivative asymmetric component e.sub.F1 related to eNOX 3 14 eNMB vector e.sub.3 [4.1] 3 14.1 eNMB proportional asymmetric component e.sub.P1 [4.2] 3 14.2 eNMB integral symmetric component 3 14.3 filtered derivative asymmetric component e.sub.D1 relating to the eNMB j: refers to the subscript of the vector considered; Ref. indicates the number that appears in the figures; Symbol: Identifies the variable by its name; Eq. indicates the equation of the general description where it has been defined. [0219] 6) Variables that refer to the multivariable and synergistic MIMO-PID controller (16); to the MISO-PID control subsystems (25, 26, 27) and to the SISO-PID controllers.

[00024] $[PID] = [\begin{matrix} C_{1} \\ C_{2} \\ C_{3} \end{matrix}] = [\begin{matrix} C_{11} & C_{12} & C_{13} \\ C_{21} & C_{22} & C_{23} \\ C_{3 1} & C_{3 2} & C_{33} \end{matrix}],$

[0220] Where [PID] is the array of PID controllers that make up the MIMO-PID multivariable and synergistic controller (16); C.sub.1 is the vector of PID controllers that make up the MISO-PID-ePPF control subsystem (25); C.sub.2 is the vector of PID controllers that make up the MISO-PID-eRMF control subsystem (26); C.sub.3 is the vector of PID controllers that make up the MISO-PID-eNMB control subsystem (27); C.sub.ij is the SISO-PID controller that relates the error associated with the monitor i to the drug j.

TABLE-US-00007 i, j Ref. PID Controller Description Symbol Eq. 1 25 MISO: ePPF .fwdarw. eBIS, eNOX, eNMB C.sub.1 [5.3] 1.1 25.1 SISO: ePPF .fwdarw. eBIS C.sub.11 1.2 25.2 SISO: ePPF .fwdarw. eNOX C.sub.12 1.3 25.3 SISO: ePPF .fwdarw. eNMB C.sub.13 2 26 MISO: eRMF .fwdarw. eBIS, eNOX, eNMB C.sub.2 2.1 26.1 SISO: eRMF .fwdarw. eBIS C.sub.21 2.2 26.2 SISO: eRMF .fwdarw. eNOX C.sub.22 2.3 26.3 SISO: eRMF .fwdarw. eNMB C.sub.23 3 27 MISO: eRCN .fwdarw. eBIS, eNOX, eNMB C.sub.3 3.1 27.1 SISO: eRCN .fwdarw. eBIS C.sub.31 3.2 27.2 SISO: eRCN .fwdarw. eNOX C.sub.32 3.3 27.3 SISO: eRCN .fwdarw. eNMB C.sub.33 i, j: refers to the subscript(s) of the controller(s) considered; Ref. indicates the number that appears in the figures; Symbol: Identifies the variable by its name; Eq. indicates the equation of the general description where it has been defined.

[0221] The system of the present invention is updated every T.sub.s seconds, the time associated with the sampling period. The system starts at time t.sub.i with the IPh. From the moment of switching from the IPh to the MPh (t.sub.m) the values are updated each period of execution of the method (T.sub.s) according to equations 2 to 13, as detailed below:

[0222] Equation 2: Measure the monitoring variables vector (6); define and calculate the vector of infusions of the manual protocol (4); and set the vector of objectives of the monitored variables (8). These are the results obtained in step 2.

TABLE-US-00008 Ref. Mathematical Definition Eq. 6 y = [y.sub.1 y.sub.2 y.sub.3].sup.T, [2.1] 4 u.sub.ol = [u.sub.ol1 u.sub.ol2 u.sub.ol3].sup.T, [2.2] u.sub.ol(t) = d(t.sub.i) + r.sub.o1 (t), [2.3] [00025] $d (t_{i}) = \frac{D .Math. W}{T_{s}},$ [2.4] D = [D.sub.1 D.sub.2 D.sub.3].sup.T, [00026] $r_{ol} (t) = {\begin{matrix} R .Math. W, & t_{i} \leq t < t_{m} \\ R .Math. W - P .Math. (t - t_{m}), & t_{m} \leq t < t_{r} \end{matrix},$ [2.5] R = [R.sub.1 R.sub.2 R.sub.3].sup.T, P = [P.sub.1 P.sub.2 P.sub.3].sup.T, 8 y.sub.T = [y.sub.T1 y.sub.T2 y.sub.T3].sup.T, [0223] Ref indicates the number that appears in the figures; Eq. indicates the equation of the general description where it has been defined, particularized for s=r=3.

[0224] Equation 3: Calculate the filtered monitoring variables vector (10), define a 5 filter bank (9) based on low-pass filters of order 1; and define a cut-off frequency vector. The result of step 3 is the filtered monitoring variables vector (10).

TABLE-US-00009 Ref. Mathematical Definition Eq. 10 y.sub.f = [y.sub.f1 y.sub.f2 y.sub.f3].sup.T, [3.1] 9 [00027] $H (f) = \frac{1}{1 + \frac{j .Math. f}{f_{c}}},$ [3.2] f.sub.c = [f.sub.1 f.sub.2 f.sub.3].sup.T [3.3] [00028] $f_{c} = \frac{N}{T_{D}},$ [3.4] Ref. indicates the number that appears in the figures; Eq. indicates the equation of the general description where it has been defined, particularized for s = r = 3.

[0225] Equation 4: Calculate the eBIS e.sub.1 monitor control error vector (12) with the DoH error generator (28), the eNOX e.sub.2 monitor control error vector (13) with the ANG error generator (29), and the 15 eNMB e.sub.3 monitor control error vector (14) with the RMX error generator (30), each with its proportional, integral and derivative components (12.1, 12.2, 12.3), (13.1, 13.2, 13.3) and (14.1, 14.2, 14.3); calculate the proportional asymmetric (12.1, 13.1, 14.1), integral symmetric (12.2, 13.2, 14.2) and derivative asymmetric (12.3, 13.3, 14.3) errors of each SISO-PID controller. The asymmetric errors shown in the present invention are calculated in such a way that the target vector of the monitored variables (8) is weighted by an array of coefficients [B] in the proportional action and an array of coefficients [G] in the derivative action, the weighting value of the integral action being unitary. The SynPlD control system of the present invention is defined by both [B] and [G] dependent on the monitoring variables vector (6) and the vector of targets of the monitored variables (8). The results of step 4 are the control error vectors (12, 13, 14).

TABLE-US-00010 Ref. Mathematical Definition Eq. 12 e.sub.1 = [e.sub.P1 e.sub.I1 e.sub.F1].sup.T [4.1] 13 e.sub.2 = [e.sub.P2 e.sub.I2 e.sub.F2].sup.T 14 e.sub.3 = [e.sub.P3 e.sub.I3 e.sub.F3].sup.T 37 e.sub.P1 = −β.sub.11 .Math. y.sub.T1 + y.sub.f1 [4.2] e.sub.I1 = −y.sub.T1 + y.sub.f1 [4.7] e.sub.D1 = −γ.sub.11 .Math. y.sub.T1 + y.sub.f1 [00029] $e_{F 1} = e_{D 1} - \frac{T_{D 1 1}}{N} \frac{{de}_{F 1}}{dt}$ 38 e.sub.P2 = −β.sub.22 .Math. y.sub.T2 + y.sub.f2 [4.2] e.sub.P2 = −y.sub.T2 + y.sub.f2 [4.8] e.sub.P2 = −γ.sub.22 .Math. y.sub.T2 + y.sub.f2 [00030] $e_{F 2} = e_{D 2} - \frac{T_{D 2 2}}{N} \frac{{de}_{F 2}}{dt}$ 39 e.sub.P3 = −β.sub.33 .Math. y.sub.T3 + y.sub.f3 [4.2] e.sub.I3 = −y.sub.T3 + y.sub.f3 [4.9] e.sub.D3 = −γ.sub.33 .Math. y.sub.T3 + y.sub.f3 [00031] $e_{F 3} = e_{D 3} - \frac{T_{D 3 3}}{N} \frac{{de}_{F 3}}{dt}$ [00032] $β = {\begin{matrix} \frac{- y_{f} - y_{thb}}{y_{T} - y_{thb}} + 2, & y_{f} < y_{T} \\ 1, & y_{f} \geq y_{T} \end{matrix}$ [4.4] [00033] $γ = {\begin{matrix} \frac{- y_{f} - y_{thg}}{y_{T} - y_{thg}} + 2, & y_{f} < y_{T} \\ 1, & y_{f} \geq y_{T} \end{matrix}$ [4.6] y.sub.thb = [y.sub.thb1 .sub.ythb2 y.sub.thb3].sup.T y.sub.thg = [y.sub.thg1 .sub.ythg2 y.sub.thg3].sup.T Ref. indicates the number that appears in the figures; Eq. indicates the equation of the general description where it has been defined, particularized for s = r = 3.

[0226] Equation 5: Calculate the control infusion vector (15) by means of a multivariable and MIMO-PID-synergized controller (16), the control error vectors of the eBIS monitor (12), eNOX (13) and eNMB (14) and the manual protocol infusion vector (4); define the [PID] matrix of SISO-PID controllers that make up the multivariable and MIMO-PID-synergized controller (16); define the SISO-PID Cu controllers relative to the j monitor and the drug i; define the proportional, integral and derivative control actions of the SISO-PID controllers whose sum defines the total control action of each SISO-PID controller; define the gain matrices relative to the SISO-PID controllers; define the drug synergy matrix; define the K.sub.ol gain vector on the manual protocol infusion vector (4); define the MISO-PID control subsystems (25, 26, 27). The result of Step 5 is the control infusion vector (15).

TABLE-US-00011 Ref. Mathematical Definition Eq. 15 u.sub.pid = [u.sub.1 u.sub.2 u.sub.3].sup.T [5.1] u.sub.pid = [PID] .Math. [SYN] + K.sub.ol .Math. u.sub.ol [5.2] 16 [00034] $[PID] = [\begin{matrix} C_{11} & C_{12} & C_{13} \\ C_{21} & C_{22} & C_{23} \\ C_{31} & C_{32} & C_{33} \end{matrix}] = [\begin{matrix} C_{1} \\ C_{2} \\ C_{3} \end{matrix}]$ [5.3] C.sub.ij = P.sub.ij + I.sub.ij + D.sub.ij [5.14] P.sub.ij = K.sub.Pij .Math. e.sub.Pj [5.15] I.sub.ij = K.sub.fij∫e.sub.Ijdτ [00035] $D_{ij} = K_{Dij} \frac{{de}_{Fj}}{dt}$ [00036] $[K_{p}] = [\begin{matrix} K_{P 11} & K_{P 12} & .Math. & K_{P 1 r} \\ K_{P 21} & K_{P 22} & .Math. & K_{P 2 r} \\ .Math. & .Math. & ⋱ & .Math. \\ K_{P s 1} & K_{P s 2} & .Math. & K_{P sr} \end{matrix}] = [\begin{matrix} K_{P 1} \\ K_{P 1} \\ .Math. \\ K_{P s} \end{matrix}]$ [5.4] [00037] $[K_{I}] = [\begin{matrix} K_{I 11} & K_{I 12} & .Math. & K_{I 1 r} \\ K_{I 21} & K_{I 22} & .Math. & K_{I 2 r} \\ .Math. & .Math. & ⋱ & .Math. \\ K_{Ix 1} & K_{Ix 2} & .Math. & K_{Isr} \end{matrix}] = [\begin{matrix} K_{I 1} \\ K_{I 2} \\ .Math. \\ K_{Is} \end{matrix}]$ [5.5] [00038] $[K_{D}] = [\begin{matrix} K_{D 11} & K_{D 12} & .Math. & K_{D 1 r} \\ K_{D 21} & K_{D 22} & .Math. & K_{D 2 r} \\ .Math. & .Math. & ⋱ & .Math. \\ K_{Ds 1} & K_{Ds 2} & .Math. & K_{Dsr} \end{matrix}] = [\begin{matrix} K_{D 1} \\ K_{D 2} \\ .Math. \\ K_{D s} \end{matrix}]$ [5.6] K.sub.P = diag([K.sub.P]) [5.7] K.sub.I = diag([K.sub.I]) [5.8] K.sub.D = diag([K.sub.D]) [5.9] [00039] $[SYN] = {[\begin{matrix} S_{11} & S_{12} & .Math. & S_{1 r} \\ S_{21} & S_{22} & .Math. & S_{2 r} \\ .Math. & .Math. & ⋱ & .Math. \\ S_{s 1} & S_{s 2} & .Math. & S_{sr} \end{matrix}]}^{T} = {[\begin{matrix} S_{1} \\ S_{2} \\ .Math. \\ S_{s} \end{matrix}]}^{T}$ [5.10] K.sub.o1 = [K.sub.ol1 K.sub.ol2 K.sub.ol3].sup.T [5.11] [00040] $K_{ol} = {\begin{matrix} 1, & y_{T} \leq y_{f}, \\ \frac{y_{f}}{y_{T} - y_{LO}} - \frac{y_{LO}}{y_{T} - y_{LO}}, & y_{LO} \leq y_{f} \leq y_{T}, \\ 0, & otherwise, \end{matrix}$ [5.12] 25 u.sub.1 = C.sub.11 .Math. S.sub.11 + C.sub.12 .Math. S.sub.12 + C.sub.13 .Math. S.sub.13 + K.sub.ol1 .Math. u.sub.ol1 [5.13] C.sub.1 = [C.sub.11 C.sub.12 C.sub.13] 26 u.sub.2 = C.sub.21 .Math. S.sub.21 + C.sub.22 .Math. S.sub.22 + C.sub.23 .Math. S.sub.23 + K.sub.ol2 .Math. u.sub.ol2 C.sub.2 = [C.sub.21 C.sub.22 C.sub.23] 27 u.sub.3 = C.sub.31 .Math. S.sub.31 + C.sub.32 .Math. S.sub.32 + C.sub.33 .Math. S.sub.33 + K.sub.ol3 .Math. u.sub.ol3 C.sub.3 = [C.sub.31 C.sub.32 C.sub.33] Ref. indicates the number that appears in the figures; Eq. indicates the equation of the general description where it has been defined, particularized for s = r = 3.

[0227] Equation 6: Define and calculate drug corrective infusions (18) from the filtered monitoring variables vector (10); define the vectors of the upper and lower thresholds of activation of the correction. The result of step 6 is the correction vector (18).

TABLE-US-00012 Ref. Mathematical Definition Eq. 18 u.sub.cr = [u.sub.cr1 u.sub.cr2 u.sub.cr3].sup.T [6.1] 17 [00041] $u_{cr} = {\begin{matrix} K_{P}^{T} .Math. [SYN] .Math. [- y_{m} + y_{f}] & y_{HI} \leq y_{f} \\ K_{P}^{T} .Math. [SYN] .Math. [- y_{LO} + y_{f}] & y_{LO} \geq y_{f} \\ 0, & otherwise \end{matrix}$ [6.2] 18.1 [00042] $u_{cr 1} = {\begin{matrix} K_{P 1}^{T} .Math. [S_{1}] .Math. [- y_{HI} + y_{f}] & y_{HI} \leq y_{f} \\ K_{P 1}^{T} .Math. [S_{1}] .Math. [- y_{LO} + y_{f}] & y_{LO} \geq y_{f} \\ 0, & otherwise \end{matrix}$ 18.2 [00043] $u_{cr 2} = {\begin{matrix} K_{P 2}^{T} .Math. [S_{2}] .Math. [- y_{HI} + y_{f}] & y_{HI} \leq y_{f} \\ K_{P 2}^{T} .Math. [S_{2}] .Math. [- y_{LO} + y_{f}] & y_{LO} \geq y_{f} \\ 0, & otherwise \end{matrix}$ 18.3 [00044] $u_{cr 3} = {\begin{matrix} K_{P 3}^{T} .Math. [S_{3}] .Math. [- y_{HI} + y_{f}] & y_{HI} \leq y_{f} \\ K_{P 3}^{T} .Math. [S_{3}] .Math. [- y_{LO} + y_{f}] & y_{LO} \geq y_{f} \\ 0, & otherwise \end{matrix}$ y.sub.HI = [H.sub.1 H.sub.2 H.sub.3].sup.T [6.3] y.sub.LO = [L.sub.1 L.sub.2 L.sub.3].sup.T [6.4] Ref. indicates the number that appears in the figures; Eq. indicates the equation of the general description where it has been defined, particularized for s = r = 3.

[0228] Equation 7: Define and calculate the reliable control vector (20), to avoid the over/under dosing of drugs, from the control infusion vector (15) and the correction vector (18); define the vectors of upper and lower limits of the drug infusion according to two concepts: 1) only positive infusions can be applied to the patient (1), therefore, the infusion pump system (2) is suspended (null infusion) when the control infusion vector (15) plus the correction vector (18) results in negative or null infusions; and 2) maximum infusion limits should avoid drug overdoses to avoid toxicity levels. The result of step 7 is the reliable control vector 20.

TABLE-US-00013 Ref. Mathematical Definition Eq. 20 u.sub.sf = [u.sub.sf1 u.sub.sf2 u.sub.sf3].sup.T [7.1] 19 [00045] $u_{sf} = {\begin{matrix} u_{HI}, & u_{pid} + u_{cr} \geq u_{HI} \\ u_{pid} + u_{cr}, & u_{L O} < u_{pid} + u_{cr} \leq u_{HI} \\ u_{L O}, & u_{pid} + u_{cr} \leq u_{L O} \end{matrix}$ [7.2] 20.1 [00046] $u_{sf 1} = {\begin{matrix} u_{H 1}, & u_{1} + u_{cr 1} \geq u_{H 1} \\ u_{1} + u_{cr 1}, & u_{L 1} < u_{1} + u_{cr 1} \leq u_{H 1} \\ u_{L 1}, & u_{1} + u_{cr 1} \leq u_{L 1} \end{matrix}$ 20.2 [00047] $u_{sf 2} = {\begin{matrix} u_{H 2}, & u_{2} + u_{cr 2} \geq u_{H 2} \\ u_{2} + u_{cr 2}, & u_{L 2} < u_{2} + u_{cr 2} \leq u_{H 2} \\ u_{L 2}, & u_{2} + u_{cr 2} \leq u_{L 2} \end{matrix}$ 20.3 [00048] $u_{sf 3} = {\begin{matrix} u_{H 3}, & u_{3} + u_{cr 3} \geq u_{H 3} \\ u_{3} + u_{cr 3}, & u_{L 3} < u_{3} + u_{cr 3} \leq u_{H 3} \\ u_{L 3}, & u_{3} + u_{cr 3} \leq u_{L 3} \end{matrix}$ u.sub.HI = [u.sub.H1 u.sub.H2 u.sub.H].sup.T [7.3] u.sub.LO = [u.sub.L1 u.sub.L2 u.sub.L3].sup.T [7.4] Ref. indicates the number that appears in the figures; Eq. indicates the equation of the general description where it has been defined, particularized for s = r = 3.?

[0229] Equation 8: Define and calculate the automatic infusion vector (22) from the reliable control vector (20); define the resolution vector of each drug infusion pump; the reliable control vector (20) is adapted to the resolution of the continuous infusion pump system (2) to thereby obtain the automatic infusion vector (22) that is dispensed to the patient (1). The result of step 8 is the automatic infusion vector 22.

TABLE-US-00014 Ref. Mathematical Definition Eq. 22 u.sub.c1 = [u.sub.cl1 u.sub.cl2 u.sub.cl3].sup.T [8.1] 21 [00049] $u_{cl} = round (\frac{u_{sf}}{u_{mn}}) .Math. u_{mn}$ [8.2] 22.1 [00050] $u_{cl 1} = round (\frac{u_{sf 1}}{u_{mn 1}}) .Math. u_{mn 1}$ 22.2 [00051] $u_{cl 2} = round (\frac{u_{sf 2}}{u_{mn 2}}) .Math. u_{mn 2}$ 22.3 [00052] $u_{cl 3} = round (\frac{u_{sf 3}}{u_{mn 3}}) .Math. u_{mn 3}$ u.sub.mn = [u.sub.mn1 u.sub.mn2 u.sub.mn3].sup.T [8.3] Ref. indicates the number that appears in the figures; Eq. indicates the equation of the general description where it has been defined, particularized for s = r = 3.

[0230] Equation 9: Define and calculate the anesthetic drug infusion vector (5) from the manual protocol infusion vector (4) and the automatic infusion vector (22) from a switching system (23). The result of step 9 is the anesthetic drug infusion vector (5).

TABLE-US-00015 Ref. Mathematical Definition Eq. 5 u.sub.pt = [u.sub.pt1 u.sub.pt2 u.sub.pt3].sup.T [9.1] 25 C.sub.1 = [C.sub.11 C.sub.12 C.sub.13].sup.T [10.1] 15.1 u.sub.1 = C.sub.1 .Math. S.sub.1 + K.sub.ol1 .Math. u.sub.ol1 [10.2] u.sub.1 = C.sub.11 .Math. S.sub.11 + C.sub.12 .Math. S.sub.12 + C.sub.13 .Math. S.sub.13 + K.sub.ol1 .Math. u.sub.ol1 Ref. indicates the number that appears in the figures; Eq. indicates the equation of the general description where it has been defined, particularized for s = r = 3.

[0231] Equation 10: Define the MISO-PID-ePPF control subsystem (25) as a set of SISO-PID controllers; calculate the control infusion of the ePPF (15.1); from the SISO-PID controllers (25.1, 25, 0.2, 25.3), from the control error vectors (12, 13, 14) and from the infusion of ePPF of the manual protocol (4.1). The result of step 10 is the ePPF control infusion (15.1).

TABLE-US-00016 Ref. Mathematical Definition Eq. 25 C.sub.1 = [C.sub.11 C.sub.12 C.sub.13].sup.T [10.1] 15.1 u.sub.1 = C.sub.1 .Math. S.sub.1 + K.sub.ol1 .Math. u.sub.ol1 [10.2] u.sub.1 = C.sub.11 .Math. S.sub.11 + C.sub.12 .Math. S.sub.12 + C.sub.13 .Math. S.sub.13 + K.sub.ol1 .Math. u.sub.ol1 Ref. indicates the number that appears in the figures; Eq. indicates the equation of the general description where it has been defined, particularized for s = r = 3.

[0232] Equation 11: Define the MISO-PID-eRMF control subsystem (26) as a set of SISO-PID controllers; calculate the control infusion of the eRMF (15.2); from the SISO-PID controllers (26.1, 26, 0.2, 26.3), from the control error vectors (12, 13, 14) and from the infusion of ePPF of the manual protocol (4.2). The result of step 11 is the control infusion of the eRMF (15.2).

TABLE-US-00017 Ref. Mathematical Definition Eq. 26 C2 = [C.sub.21 C.sub.22 C.sub.23].sup.T [11.1] U.sub.2 = C.sub.2 .Math. S.sub.2 + K.sub.ol2 .Math. u.sub.ol2 [11.2] text missing or illegible when filed Ref. indicates the number that appears in the figures; Eq. indicates the equation of the general description where it has been defined, particularized for s = r = 3. indicates data missing or illegible when filed

[0233] Equation 12: Define the MISO-PID-eNMB control subsystem (27) as a set of SISO-PID controllers; calculate the control infusion of the eRCN (15.3); from the SISO-PID controllers (27.1, 27, 0.2, 27.3), from the control error vectors (12, 13, 14) and from the infusion of eRCN of the manual protocol (4.3). The result of step 11 is the eRCN control infusion (15.3).

TABLE-US-00018 Ref. Mathematical Definition Eq. 27 C.sub.3 = [C.sub.31 C.sub.32 C.sub.33].sup.T [10.1] 15.3 u.sub.3 = C.sub.3 .Math. S.sub.3 + K.sub.ol3 .Math. u.sub.ol3 [10.2] u.sub.3 = C.sub.31 .Math. S.sub.31 + C.sub.32 .Math. S.sub.32 + C.sub.33 .Math. S.sub.33 + K.sub.ol3 .Math. u.sub.ol3 Ref. indicates the number that appears in the figures; Eq. indicates the equation of the general description where it has been defined, particularized for s = r = 3.

[0234] Equation 13: It is formulated for the claiming of new drugs and monitors.

TABLE-US-00019 Ref. Mathematical Definition Eq. C.sub.i = [C.sub.i1 C.sub.i2 . . . C.sub.ir].sup.T [13.1] u.sub.i = C.sub.i .Math. S.sub.i + K.sub.oli .Math. u.sub.oli [13.2] Ref. indicates the number that appears in the figures; Eq. indicates the equation of the general description where it has been defined, particularized for s = r = 3.

[0235] Equation 14: Adjustment of the parameters of the SynPlD control system to each patient to absorb inter/intra-patient variability. The adjustment involves step 3 and step 5 of the invention. In step 3 the cut-off frequencies of the filters expressed in the equations [3.5] and [3.6] have to be adjusted. In step 5, the MIMO-PID (16) multivariable and synergistic controller gain matrices expressed in the equations [5.16], [5.17] and [5.18] have to be adjusted.

[0236] The information necessary for the adjustment is extracted in the IPh where the patient (1) is subjected only to the manual protocol infusion vector (4). Thus, from t.sub.i to t.sub.m the method executions are stored in a memory and just at time t.sub.m a variant of the Cohen-Coon PID empirical adjustment method is applied and at that instant the switching system (23) the anesthetic drug infusion vector (5) is the result of the SynPlD control system 24 according to the automatic infusion vector (22). The gains relating to the proportional gain matrix [5.19] are related to the patient's weight and the manual protocol infusion vector (4) are related to the patient's weight, height, gender and muscle mass.

[0237] Equation 14 involves all the steps defined for the proposed invention, but directly involves especially steps 3, 5 and 6. Once the gains in time t.sub.m have been calculated, they will remain constant until the recovery time t.sub.r.

[0238] The systems that need adjustment are: [0239] Filter bank (9): it is necessary to adjust the three parameters that refer to the cutoff frequency. [0240] Multivariable controller with MIMO-PID synergy (16): it is necessary to adjust 18 parameters, for each SISO-PID controller a proportional, an integral and a derivative gain must be adjusted. [0241] Infusion correction system (17): which uses the proportional gains of the MIMO-PID multivariable and synergistic controller (16).

TABLE-US-00020 Ref. Mathematical Definition Eq. 9 f.sub.c = [f.sub.1 f.sub.2 f.sub.3].sup.T [3.3] 9 [00053] $f_{c} + \frac{N}{T_{D}}$ [3.4] 16 [00054] $[K_{P}] = [\begin{matrix} K_{P 11} & K_{P 12} & .Math. & K_{P 1 r} \\ K_{P 21} & K_{P 22} & .Math. & K_{P 2 r} \\ .Math. & .Math. & ⋱ & .Math. \\ K_{Ps 1} & K_{Ps 2} & .Math. & K_{Psr} \end{matrix}] = [\begin{matrix} K_{P 1} \\ K_{P 2} \\ .Math. \\ K_{Ps} \end{matrix}]$ [5.4] 16 [00055] $[K_{I}] = [\begin{matrix} K_{I 1 1} & K_{I 1 2} & .Math. & K_{I 1 r} \\ K_{I 2 1} & K_{I 2 2} & .Math. & K_{I 2 r} \\ .Math. & .Math. & ⋱ & .Math. \\ K_{Is 1} & K_{Is 2} & .Math. & K_{Isr} \end{matrix}] = [\begin{matrix} K_{I 1} \\ K_{I 2} \\ .Math. \\ K_{I s} \end{matrix}]$ [5.5] 16 [00056] $[K_{D}] = [\begin{matrix} K_{D 11} & K_{D 12} & .Math. & K_{D 1 r} \\ K_{D 21} & K_{D 22} & .Math. & K_{D 2 r} \\ .Math. & .Math. & ⋱ & .Math. \\ K_{Ds 1} & K_{Ds 2} & .Math. & K_{Dsr} \end{matrix}] = [\begin{matrix} K_{D 1} \\ K_{D 2} \\ .Math. \\ K_{Ds} \end{matrix}]$ [5.6] 24 [00057] $SYN = {[\begin{matrix} S_{1 1} & S_{1 2} & S_{1 3} \\ S_{2 1} & S_{2 2} & S_{2 3} \\ S_{31} & S_{3 2} & S_{3 3} \end{matrix}]}^{T} = {[\begin{matrix} S_{1} \\ S_{2} \\ S_{3} \end{matrix}]}^{T}$ [5.10] K.sub.P = W.sup.−1 .Math. K K.sub.I = T.sub.I.sup.−1 × K.sub.P K.sub.D = K.sub.P × T.sub.D T.sub.I = diag([T.sub.I]) T.sub.D = diag([T.sub.D]) Ref. indicates the number that appears in the figures; Eq. indicates the equation of the general description where it has been defined, particularized for s = r = 3. “x” identifies the product element by element (Schur product).

[0242] Where K is a Universal Gain [(μg/(min))/UM]; [K.sub.P] is the Proportional Gain Matrix [(m/(min kg))/UM]; [K.sub.I] is the Integral Gain Matrix [(μg (min kg))]/UM min)]; [T.sub.I] is the integral action time matrix [min]; [K.sub.D] is the derivative gain matrix [(m/kg)/UM]; [T.sub.D] is the derivative action time matrix [min].

[0243] Equation 15: Adjustment of other parameters of the SynPlD control system that are universal and common to all patients (common to inter/intra-patient variability). These parameters are generally constant, but can be explicitly and generally adjusted by sex, population groups (diabetics, obese persons . . . ), types of surgery, territories and/or ages.

[0244] Below are the parameters considered and the equations in which they appear:

TABLE-US-00021 Para- Eq. meter Type Definition Eq. W Scalar Patient Weight [2.4] T.sub.s Scalar Method execution period [2.4] y.sub.T Vector Objectives on the monitors [2.6] y.sub.T = [50 30 10].sup.T D Vector Boluses per unit of mass [2.4] D = [700 0.5 500].sup.T; (μg/kg) R Vector Constant induction infusion [2.5] R = [100 0.3 2000].sup.T; (μg/(kg .Math. min)) P Vector Decreased infusion in IPh [2.5] □ = [3.2 0.69 0].sup.□; ((ng/(kg min))/s) N Scalar PID filtering coefficient [3.4] N = 12 y.sub.thb Vector Thresholds determining the maximum [4.4] value of p y.sub.thb = [40 20 0].sup.T y.sub.thg Vector Thresholds determining the maximum [4.6] value of y y.sub.thg = [45 25 5].sub.T [SYN] Matrix Drug synergy [5.10] [00058] $[SYN] = {[\begin{matrix} 1 & 1 & 0 \\ 1 & 1 & 0 \\ 0 & 0 & 1 \end{matrix}]}^{T}$ y.sub.HI Vector Upper thresholds for the activation of the [6.3] correction y.sub.HI = [60 40 20].sup.T y.sub.LO Vector lower thresholds for the activation of the [6.4] correction y.sub.LO = [40 20 0].sup.T u.sub.HI Vector Upper infusion limits [7.3] u.sub.HI = [100 0.3 2000].sup.T; (μg/(kg .Math. min)) u.sub.LO Vector Lower infusion limits [7.4] u.sub.LO = [0 0 0].sup.T; (μg/(kg .Math. min)) u.sub.min Vector Pump Resolutions [8.3] u.sub.mn = [u.sub.mn1 u.sub.mn2 u.sub.mn3].sup.T; (μg/(kg .Math. min)) Hardware dependent t.sub.i Scalar IPh Start Time [9.2] t.sub.m Scalar MPh Start Time [9.2] t.sub.r Scalar RPh Start Time [9.2]

Industrial Application

[0245] Regarding the implementation of the multi-infusion drug system with synergy by control in CL MIMO-PID (SynPlD) applied in IV anesthetic acts of the present invention, one of the embodiments contemplates its execution in code interpretable by Android, IOS, Arduino, Raspberry-PI devices, personal computers, commercial infusion pumps or specific hardware.

[0246] The SynPlD control system runs iteratively every T.sub.s seconds and consists of the following phases: 1) Startup at t.sub.j where there are N.sub.j iterations of the method; 2) IPh, from t.sub.i to t.sub.m, where there are N.sub.m-N.sub.i iterations of the method; 3) MPh, from t.sub.m to t.sub.r, and 4) RPh, from t.sub.r, where there are N.sub.r-N.sub.m iterations of the method. The result of each iteration is to obtain the anesthetic drug infusion vector (5) to be administered to the patient (1) with the infusion pump system (2) so that the monitoring variables vector (6) is led to the target vector of the monitored variables (8) despite the vector of surgical actions (7).

[0247] The actions to be taken in each of the phases of application of the SynPlD method implemented in any of the aforementioned devices are: [0248] 1) Before startup, the following data must be obtained: 1) of the patient (1), the weight, age, sex, height and muscle mass to adjust the manual protocol infusion vector (4) from which the values will be derived of the vectors of equations [2.4] and [2.5]; 2) of the infusion pump system (2) its resolution to adjust the vector of the equation [8.3]; 3) of the SynPlD system (24) its execution period shown in equation [2.4]; the drug synergy matrix shown in equation [5.10]; the asymmetry matrices of the control errors shown in equations [4.3] and [4.5] and in general of all the parameters shown in equation 15. [0249] 2) In the IPh the anesthetic drug infusion vector (5) that is administered to the patient (1) corresponds to the manual protocol infusion vector (4) as shown in equation [2.3], [0250] 3) At the end of the IPh, the adjustment is carried out of the gain matrices shown in equations [5.4], [5.5] and [5.6] and in general of all the parameters shown in equation 14. [0251] 4) In MPh, the anesthetic drug infusion vector (5) administered to the patient (1) corresponds to the automatic infusion vector (22). [0252] 5) In RPh the anesthetic drug infusion vector (5) that is administered to the patient (1) is annulled.

[0253] In an indeterminate Iteration of the SynPlD control system, the following actions must be followed in the given order: [0254] 1. Start by acquiring the information of the monitoring variables vector (6) using the monitoring equipment (3), the information acquired in the current iteration and in the past iterations are stored in a memory for later use; the information obtained in this action will serve for the feedback of the control system in CL; [0255] 2. The target vector of the monitored variables must be set (8); the result of the current iteration and the past iterations are stored in a memory for later use; [0256] 3. Noise and artifacts from the monitoring variable vector (6) must be cleaned by means of a filter bank (9) to obtain the monitoring filtered variable vector (10); the result of the current iteration and the past iterations are stored in a memory for later use; [0257] 4. The control error vectors (12, 13, 14) must be calculated by means of a control error generator (11); each control error vector is formed by a proportional asymmetric error, an integral symmetric error and a derivative asymmetric error; using the current instants of the target vector of the monitored variables (8) and of the filtered monitoring variables vector (10); the result of the current iteration and of the past iterations are stored in a memory for later use; [0258] 5. The control infusion vector (15) must be calculated by means of a multivariable controller with MIMO-PID synergy (16) that has as inputs the control error vectors (12, 13, 14) and the manual protocol infusion vector (4); from each SISO-PID controller the contribution to each drug of each control error distributed in a proportional action that makes use of the current iteration, an integral action that makes use of the current iteration and all the past ones and a derivative action that makes use of the current iteration and the previous one is obtained; the result of the current iteration and of the past iterations are stored in a memory for later use; [0259] 6. A correction vector (18) must be calculated by means of an infusion correction system (17) that presents at its input the filtered monitoring variables vector (10) of the current Iteration; the result of the current iteration and of the past iterations are stored in a memory for later use; [0260] 7. A reliable control vector (20) must be calculated by means of a security system (19) that presents at its input the current Iteration of the control infusion vector (15) and the correction vector (18); the result of the current Iteration and the past Iterations are stored in a memory for later use; [0261] 8. A vector of automatic infusions (22) must be calculated by means of a quantification system (21) that presents at its input the current iteration of the reliable control vector (20); the result of the current iteration and the past iterations are stored in a memory for later use; [0262] 9. The anaesthetic drug infusion vector (5) must be defined by means of a switching system (23) that has as inputs the current iteration of the control infusion vector (15), of the manual protocol infusion vector (4) and of a zero vector; the result of the current iteration and of the past iterations are stored in a memory for later use; [0263] 10. Once the anesthetic drug infusion vector (5) has been obtained, a new iteration begins, returning to point one of this list and increasing the number of the iteration.

[0264] The multi-infusion system of anesthetic drugs with synergy by control in CL MIMO-PID (SynPlD) applied in IV anesthetic acts of the present invention, is prepared for installation in intelligent infusion pumps that make use of an electronic circuit based on a microprocessor with memory, configured to determine the anesthetic drug infusion vector (5) to administer to the patient (1) by IV in a surgical act; each iteration period Lis configurable between 1 and 30 seconds. The mission of the anesthetic drug infusion vector (5) is to obtain a satisfactory anesthetic state given by the target vector of the monitored variables (8).

[0265] The SynPlD control system is configured to carry out the method presented in the invention that is repeated every T.sub.s seconds, where the system comprises executing the following blocks: [0266] 1. Memory: Memorization of the data history of the information from the monitoring equipment (3), the target vector of the monitored variables (8), filtered monitoring variables vector (10), vectors of control errors (12, 13, 14), vector of control infusions (15), correction vector (18), reliable control vector (20) and anesthetic drug infusion vector (5) performed by the infusion pump system (2) and the computer code based on the instructions necessary to execute the SynPlD method in a processor; [0267] 2. Counter: An electronic time counter for determining the phases IPh, MPh and RPh, estimating that IPh lasts between 5 and 10 minutes, that MPh is very variable in duration depending on the surgery and that RPh lasts between 10 and 20 minutes; this counter implements the switching system (23) in the form of computer code and the relevant instructions to execute them in the processor; [0268] 3. Adjustment: A software routine that is executed at time t.sub.m to adjust the parameters of the SynPlD control system to each patient (1) using the actions shown in equation 14 and the system memory; [0269] 4. Routine 1: Reusable software routine for the implementation of the filter bank (9) using the gain matrix [K.sub.D] shown in equation [5.6] and the filtering coefficient N presented in equation [3.4]; the routine can be executed several times with modifiable input parameters, the routine makes use of the information stored in the memory; [0270] 5. Routine 2: Reusable software routine for the implementation of the control error generator (11) making use of the elements of the matrices [B] and [G] given in equations [4.3] and [4.5], whose values are between 1 and 2; a single routine is implemented with the code for obtaining the control error vectors (12, 13, 14), the routine can be executed several times with modifiable input parameters, the routine makes use of the information stored in the memory; [0271] 6. Routine 3: Reusable software routine for the implementation of the multivariable and synergistic MIMO-PID controller (16) using the SISO-PID controllers defined in equations [5.14] and [5.15]; a single routine is implemented with the execution code of the SISO-PID controllers that form the three MISO-PID control subsystems (25, 26, 27) and that in turn form the multivariable controller and with MIMO-PID synergy (16), the routine can be executed several times with modifiable input parameters, the routine makes use of the information stored in the memory; [0272] 7. Routine 4: Software routine that calculates the control infusion vector (15) with the results obtained by applying Routine 3 relating to the results of the SISO-PID controllers and the manual protocol infusion vector (4), shown in the equations [5.14] and [2.4] [2.5]; a single routine is implemented with the execution code to obtain the control infusion vector (15), the routine makes use of the information stored in the memory; [0273] 8. Routine 5: Reusable software routine for the implementation of the infusion correction system (17) making use of the filtered monitoring variables vector (10) shown in equation [3.2], making use of the cut-off frequencies defined in equations [3.3] and [3.4]; a single routine is implemented with the execution code of the infusion correcting system (17), the routine can be executed several times with modifiable input parameters; the routine makes use of the Information stored in the memory; [0274] 9. Routine 6: Reusable software routine for the implementation of the security system (19) making use of the control infusion vector (15) shown in equations [6.1] and [6.2], a single routine is implemented with the execution code of the security system (19), the routine can be executed several times with modifiable input parameters; the routine makes use of the information stored in the memory; [0275] 10. Routine 7: Reusable software routine for the implementation of the quantification system (21) making use of the reliable control vector (20) shown in equations [7.1] and [7.2], a single routine is implemented with the execution code of the quantification system (21); the routine can be executed several times with modifiable input parameters; the routine makes use of the Information stored in the memory.

CLOSED-LOOP DRUG INFUSION SYSTEM WITH SYNERGIC CONTROL

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A61M5/1723

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A61B5/4839

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G16H20/17

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A61M2005/14208

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A61M5/1408

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A61B5/4821

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A61M2205/50

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A61B5/1106

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A61M5/16827

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A61B5/00

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A61M5/172

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A61M5/14

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A61M5/168

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G16H20/17

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