Electrical power converter

Abstract

A power converter comprises a regulator, a value-supply system arranged for collecting at least one operating point of the power converter, and a predictor operative to produce updated regulator parameters (such as one or more power supply coefficients) implemented by the regulator to produce an output voltage to power a load. The updated regulator parameters are determined using a process based on the at least one collected operating point samples and predictor parameters obtained from a machine-learning process.

Claims

1. An apparatus comprising: a voltage regulator operative to convert an input voltage into an output voltage; a storage resource operative to store map information that provides a mapping between prior operational states of the voltage regulator and respective sets of control values that control the voltage regulator; and a controller operative to: i) receive current operational states of the voltage regulator; ii) via the map information, map the current operational states to a corresponding set of control values; and iii) apply the corresponding set of control values to the voltage regulator to convert the input voltage into the output voltage.

2. The apparatus as in claim 1, wherein the control values as specified by the map information are machine-learned control responses assigned to the respective sets of control values to maintain a magnitude of the output voltage within regulation over different operational states of the voltage regulator.

3. The apparatus as in claim 1, wherein the sets of control values are sets of control coefficients applied via the controller to process an error signal.

4. The apparatus as in claim 1, wherein the sets of control values are generated via bias and weight values associated with the voltage regulator.

5. The apparatus as in claim 1, wherein the prior operational states and current operational states of the voltage regulator include: magnitudes of the output voltage and magnitudes of an output current supplied by the output voltage to the load.

6. The apparatus as in claim 1, wherein the current operational states of the voltage converter substantially match prior samples of operational states of the voltage converter that map to the corresponding set of control values; and wherein the controller is further operative to select the corresponding set of control values based on a substantial match of the current operational states to the prior samples of operational states of the voltage converter.

7. The apparatus as in claim 1, wherein the prior operational states of the voltage regulator include first prior operational states and second prior operational states; wherein the respective sets of control values include a first set of control values and a second set of control values.

8. The apparatus as in claim 7, wherein the map information maps the first prior operational states of the voltage regulator to the first set of control values, the first set of control values providing a first control response to the voltage regulator; and wherein the map information maps the second prior operational states of the voltage regulator to the second set of control values, the second set of control values providing a second control response to the voltage regulator.

9. The apparatus as in claim 1, wherein the current samples of operational states of the voltage regulator are obtained via sampling multiple operational parameters of the voltage regulator.

10. The apparatus as in claim 9, wherein the current samples of operational settings of the voltage regulator include a respective sequence of multiple data samples for each of the multiple operational parameters of the voltage regulator collected over time.

11. The apparatus as in claim 1, wherein the received set of current operational states includes a sequence of current samples of a monitored parameter of the voltage regulator; and wherein the first set of prior operational states includes a sequence of prior samples of the monitored parameter of the voltage regulator.

12. The apparatus as in claim 11, wherein a pattern associated with the sequence of current samples of the monitored parameter match a pattern associated with the sequence of prior samples of the monitored parameter.

13. The apparatus as in claim 12, wherein the corresponding first set of control values assigned to the first set of prior operational states represents a control response to maintain a magnitude of the output voltage within a desired voltage range for the first set of prior operational states.

14. The apparatus as in claim 1, wherein the current operational states of the voltage regulator include a respective sequence of multiple data samples for each of multiple parameters of the voltage regulator collected over multiple sample times.

15. The apparatus as in claim 14, wherein the respective sequence of multiple data samples for each of the multiple parameters include: a first current sequence of buffered samples measuring a magnitude of the input voltage over multiple current sample times; a second current sequence of buffered samples measuring a magnitude of input current provided to multiple phases by the input voltage over the multiple current sample times; a third current sequence of buffered samples measuring a magnitude of the output voltage over the multiple current sample times; and a fourth current sequence of buffered samples measuring a magnitude of output current provided by the output voltage to a load over the multiple current sample times.

16. The apparatus as in claim 15, wherein the prior operational states include: a first prior sequence of buffered samples measuring a magnitude of the input voltage over multiple prior sample times; a second prior sequence of buffered samples measuring a magnitude of input current provided to multiple phases by the input voltage over the multiple prior sample times; a third prior sequence of buffered samples measuring a magnitude of the output voltage over the multiple prior sample times; and a fourth prior sequence of buffered samples measuring a magnitude of output current provided by the output voltage to a load over the multiple prior sample times.

17. The apparatus as in claim 1, wherein the voltage regulator is configured to operate at a substantially higher operating frequency to regulate a magnitude of the output voltage than a frequency of the controller mapping the current operational states to the corresponding set of control values and applying the corresponding set of control values to the voltage regulator.

18. The apparatus as in claim 1, wherein the prior operational states of the voltage regulator include a first set of prior operational states of the voltage regulator, the first set of prior operational states being one of multiple sets of prior operational states, each of the multiple sets of prior operational states being assigned a different control response.

19. The apparatus as in claim 1, wherein the controller is further operative to match the current operational states of the voltage regulator to the prior operational states of the voltage regulator to identify the corresponding set of control values.

20. A method comprising: storing map information in a repository, the map information providing a mapping between prior operational states of a voltage regulator and respective sets of control values that were previously implemented to control the voltage regulator; receiving current operational states of the voltage regulator; via the map information, mapping the current operational states to a corresponding set of control values; and applying the corresponding set of control values to the voltage regulator to convert an input voltage into an output voltage.

21. The method as in claim 20, wherein the control values as specified by the map information are machine-learned control responses assigned to the respective sets of control values to maintain a magnitude of the output voltage within regulation over different operational states.

22. The method as in claim 20, wherein the sets of control values are sets of control coefficients; and wherein applying the corresponding set of control values to the voltage regulator includes applying a set of control coefficients to an error signal of the voltage regulator.

23. The method as in claim 20, further comprising: generating the sets of control values via bias and weight values associated with the voltage regulator.

24. The method as in claim 20, wherein the prior operational states and current operational states of the power converter include sampling of a magnitude of the output voltage and a magnitude of an output current supplied by the output voltage to a load.

25. The method as in claim 20, further comprising: matching the current operational states of the voltage converter to prior samples of operational states of the voltage converter that map to the corresponding set of control values; and selecting the corresponding set of control values based on a substantial match of the current operational states to the prior samples of operational states of the voltage converter.

26. The method as in claim 20, wherein the prior operational states of the voltage regulator include first prior operational states and second prior operational states; and wherein the respective sets of control values include a first set of control values and a second set of control values.

27. The method as in claim 26, wherein the map information maps the first prior operational states of the voltage regulator to the first set of control values, the first set of control values providing a first control response; and wherein the map information maps the second prior operational states of the voltage regulator to the second set of control values, the second set of control values providing a second control response.

28. The method as in claim 20 further comprising: obtaining the current samples of operational states of the voltage regulator via sampling multiple operational parameters of the voltage regulator.

29. The method as in claim 28, wherein the current samples of operational settings of the voltage regulator include a respective sequence of multiple data samples for each of the multiple operational parameters of the voltage regulator collected over time.

30. An apparatus comprising: a voltage regulator operative to convert an input voltage into an output voltage; a storage resource operative to store map information that provides a mapping between sets of prior operational states of the voltage regulator and sets of control values that control the voltage regulator; and a controller operative to: i) receive a set of current operational states of the voltage regulator; ii) match the received set of current operational states of the voltage regulator to a first set of prior operational states of the voltage regulator, the first set of prior operational states being one of the sets of prior operational states of the voltage regulator; iii) via the map information, identify a corresponding first set of control values assigned to the first set of prior operational states; and iv) apply the corresponding first set of control values to the voltage regulator to convert the input voltage into the output voltage.

31. The apparatus as in claim 30, wherein the received set of current operational states of the voltage regulator represent current sampled conditions associated with the voltage regulator; and wherein the corresponding first set of control values assigned to the first set of prior operational states represents a control response to maintain a magnitude of the output voltage within a desired voltage range for the first set of prior operational states.

32. The apparatus as in claim 30, wherein the current operational states of the voltage regulator represent current operational conditions of the voltage regulator converting the input voltage into the output voltage; and wherein the prior operational states of the voltage regulator indicate previous operational conditions of the voltage regulator detected during a machine learning process, the corresponding first set of control values representing a control response assigned to the prior operational states of the voltage regulator.

33. The apparatus as in claim 32, wherein the corresponding first set of control values include a set of power supply coefficients indicating at least one PID coefficient setting to be applied to the voltage regulator, application of the set of power supply coefficient settings to the voltage regulator operative to maintain the output voltage within a desired voltage range.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) FIG. 1 is a diagram showing elements of an electrical power converter according to the invention.

(2) FIG. 2 is an example diagram illustrating a calculation sequence implemented by a predictor according to embodiments herein.

(3) FIG. 3 is an example diagram illustrating a PID controller and application of power supply coefficients according to embodiments herein.

(4) FIG. 4 is an example diagram illustrating mapping of current operating settings of a power converter to appropriate control coefficients to achieve a desired control response according to embodiments herein.

(5) FIG. 5 is an example diagram illustrating mapping of current operating settings of a power converter to multiple sets of control coefficients and derivation of control coefficients from the multiple sets to achieve a desired control response according to embodiments herein.

(6) FIG. 6 is an example diagram illustrating use of logic to derive control coefficients to control a power converter according to embodiments herein.

(7) FIG. 7 is a diagram illustrating example computer architecture to execute one or more operations according to embodiments herein.

(8) FIG. 8 is an example diagram illustrating methods according to embodiments herein.

DETAILED DESCRIPTION

(9) For clarity sake, components and elements which are not directly concerned with embodiments herein are not described thereafter, given that one skilled in the art knows how to implement such components and elements.

(10) For illustrative purpose but without limitation to such embodiment type, embodiments herein are now described for a DC-DC power converter, and for a regulator of PID-type. But it should be understood that embodiments herein can be implemented with any type of power converter, and with any regulator type for each power converter type. Other regulator types which may be used alternatively include proportional regulators, integral regulators, derivative regulators, proportional-integral regulators, integral-derivative regulators, proportional-derivative regulators, regulators which implement at least one higher order component for controlling the power conversion, delta-regulators, delta-sigma regulators, differential regulators, etc. It is only necessary for the invention that the regulator implements at least one regulator parameter for issuing at least one signal control that is used by the power converter for producing the conversion of input voltage and input current into output voltage and output current.

(11) A DC-DC power converter in accordance with embodiments herein supplies electrical power to one or more loads such as a computer mother-board, but preferably specifically to a processor in a point-of-load configuration. For such configuration, one power converter is dedicated to one processor, and located next to it on a common printed circuit board.

(12) In a known manner, the converter as described herein comprises one or more phases connected in parallel between an input of the converter and an output operative to power a load, i.e. the processor to be power-supplied in the present example. In one embodiment, each phase may comprise two switching devices, which produce each a connecting state during on-periods and an isolating state during off-periods. Each switching device is operated through control signals, for example PWM (pulse-width modulation) signals or PFM (pulse-frequency modulation) signals, which are issued by a PID regulator. Preferably, one PID regulator is common to all the switching devices of one converter.

(13) In a known manner, conventional PID regulators (controllers) implement k.sub.p-, k.sub.i- and k.sub.d-coefficients for generating the control signals based on operation parameters of the converter. The k.sub.p-coefficient is the so-called proportional gain, the k.sub.i-coefficient is the so-called integral gain and the k.sub.d-coefficient is the so-called derivative gain. For such particular case of a PID regulator, the k.sub.p-, k.sub.i- and k.sub.d-coefficients are the regulator parameters as mentioned in the general part of the description.

(14) During operation, the converter according to embodiments herein receives an input current and an input voltage, denoted I.sub.input and V.sub.input respectively, from an external DC power source. measured values of this input current I.sub.input and input voltage V.sub.input may be collected repeatedly, for example every n-cycle operation period of the converter, n being a non-zero fixed integer such as 16, 32, 64, etc.

(15) The converter transforms this input current I.sub.input and input voltage V.sub.input into an output current I.sub.output and an output voltage V.sub.output which are transmitted to the load. Measured values of this output current I.sub.output and output voltage V.sub.output may also be collected for the same operation instants.

(16) In case of a multiphase converter, other values may be measured instead of the converter input/output voltage/current just mentioned, depending on the converter design. These other values may relate each to a current supplied to or issued by one of the phases, called phase input/output current and denoted I.sub.phase_input or I.sub.phase_output, respectively. Similarly, a voltage supplied to or produced by one of the phases, called phase input/output voltage and denoted V.sub.phase_input or V.sub.phase_output, respectively, may be used too. Such phase input/output current/voltage values may also be used in combination with some or all of the converter input values I.sub.input and V.sub.input and converter output values I.sub.output and V.sub.output.

(17) Collection of one or more of these measured values is performed by a so-called value-supply system (such as one or more sensors monitoring operational parameters of the power converter). This value-supply system gathers the measured value(s) which relate to one same instant of operation of the converter into one value set which is called operating point. Each operating point is further completed by the value-supply system with a target output voltage which also relates to the same operation instant as the measured values of this operating point. The target output voltage, denoted V.sub.target, is used by the PID regulator for generating the control signals, so that the output voltage V.sub.output which is actually produced by the converter is close to the target output voltage V.sub.target. Successive values of the target output voltage V.sub.target allow controlling variations in the instant output voltage which is supplied to the load, in particular depending on active periods or idle periods of modules internal to this load. They also allow controlling the converter output during transient periods which are intermediate between active and idle periods.

(18) The value-supply system transmits each operating point to a predictor, which determines therefrom the values for the k.sub.p-, k.sub.i- and k.sub.d-coefficients to be implemented in each PID regulator. The predictor transmits the determined k.sub.p-, k.sub.i- and k.sub.d-values to the PID regulators of the converter, so that each of these PID regulators implements the k.sub.p-k.sub.i- and k.sub.d-coefficient values related to it from an instant subsequent to their reception.

(19) More specifically, as shown in FIG. 1, reference number 10 denotes a DC-DC power converter; reference number 20 denotes the power supply which is connected to the input of the power converter 10; and reference number 30 denotes the load which is powered by the output of the power converter 10.

(20) In one nonlimiting example embodiment, the power supply 20 is of DC-type and the load may a microprocessor, a memory, a laptop, a smartphone, a tablet, a LED light bulb, a TV, etc. Each reference number 11 denotes a separate phase of the converter, whatever their number, and each reference number 12 denotes one switching device within each phase 11. The internal structure of each phase 11 is not represented in FIG. 1, and may be of any type known in the art. For example, it may be of buck converter type. For clarity of the figure, only one switching device 12 per phase has been represented. The other reference numbers are: 13: the regulator, of PID-type controller in the example considered 14: the predictor 15: the value-supply system (one or more voltage or current sensors) although it is distributed at several locations in the figure

(21) The PID regulator 13 (PID controller), the predictor 14 and the value-supply system 15 are part of the DC-DC power converter 10 together with the phases 11.

(22) The value-supply system 15 may comprise one or more voltage sensors and/or one or more current sensors, such as usual voltage and/or current sensors, for example direct current resistors for sensing the currents. These sensors may be combined with sample-and-hold units and analog-to-digital converters to issue at least some of the measured values V.sub.input, I.sub.input, V.sub.output, I.sub.output, V.sub.phase_input, I.sub.phase_input, V.sub.phase_output, I.sub.phase_output, corresponding to common instants of operation for the converter. Advantageously, the sampling period may be a multiple of the switching period of the phases 11, but the sampling period may also be selected depending on the converter application, for instance so as to update the PID parameters sufficiently fast with respect to the load changes. The sampling period may also be selected depending on the power consumption caused by each value measurement and each update of the k.sub.p-, k.sub.i- and k.sub.d-values.

(23) The measured values for at least some of V.sub.input, I.sub.input, V.sub.output, I.sub.output, V.sub.phase_input, I.sub.phase_input, V.sub.phase_output, I.sub.phase_output, and the target output voltage V.sub.target are transmitted by the value-supply system 15 (respective sensors) to the PID regulator 13 for operation of this latter in a manner as known before the present invention.

(24) According to one embodiment, the operating point(s), i.e. the measured value(s) for one or more of V.sub.input, I.sub.input, V.sub.output, I.sub.output, and optionally V.sub.phase_output and I.sub.phase_output, and the target output voltage V.sub.target, is transmitted to the predictor 14 for determining the k.sub.p-, k.sub.i- and k.sub.d-coefficient values to be implemented in the PID regulator 13.

(25) Operation of the predictor 14 is now described.

(26) Preferably, the predictor 14 includes a FIFO-queue (i.e., data buffer) like memory set for storing a fixed number of operating points which relate to successive operation instants of the converter. For example, a further operating point is issued by the value-supply system 15 at the end of every sampling time. This further operating point is stored into an entrance cell of the FIFO-queue like memory set, and all the previously stored operating points are shifted by one cell in the queue toward the last memory cell. That one of the operating points which was stored at the last memory cell of the queue is dropped. All or a portion of data in the memory set is used for determining the next values for the k.sub.p-, k.sub.i- and k.sub.d-coefficients. This allows anticipating events such as load changes, voltage changes, phase dropping and any possible event to occur by implementing in advance k.sub.p-, k.sub.i- and k.sub.d-values that are appropriate for such event.

(27) For predicting the values of the k.sub.p-, k.sub.i- and k.sub.d-coefficients in a way appropriate to each application, the predictor 14 implements an algorithm called machine-learning model. Such machine-learning model may be run within the predictor 14 as embedded software or directly in hardware, or any combination of both. This allows using a same silicon chip for any application of the converter 10. In particular, using a neuromorphic chip which implements a spiking neural network for the predictor 14 enables a very energy-efficient hardware implementation of the machine-learning model.

(28) A simple machine-learning model for the predictor 14 includes storing within the predictor a number of operating points of the power converter 10 with associated values for the k.sub.p-, k.sub.i- and k.sub.d-coefficients. Preferably, series of successive operating points are stored with associated values for the k.sub.p-, k.sub.i- and k.sub.d-coefficients.

(29) Then, each time the value-supply system 15 provides a series of actual operating points, an algorithm, such as a nearest-neighbor algorithm, determines which one of the previously stored operating point series (from machine learning) is the nearest to the series of actual operating points. The difference between the actual operating point series and any one of the stored operating point series may be calculated using any norm commonly known in the art.

(30) The values for the k.sub.p-, k.sub.i- and k.sub.d-coefficients to be implemented are then those associated with the nearest one of the stored operating point series. For such implementation, the stored operating point series with associated values for the k.sub.p-, k.sub.i- and k.sub.d-coefficients may be recorded in a lookup table which is internal to the predictor 14. They constitute so-called labelled training data, and also the predictor parameters that are used by the predictor 14 for inferring each new set of updated k.sub.p-, k.sub.i- and k.sub.d-values. Such implementation of embodiments herein is more appropriate when the converter 10 has to accommodate to a small number of operation schemes.

(31) Another possible machine-learning model may be based on regression and may use a neural network. Such regression-based implementation allows continuous changes for the k.sub.p-, k.sub.i- and k.sub.d-values and thus avoids value jumps as those which may result from the above-described nearest-neighbor implementation. A minimum calculation structure to be implemented within the predictor 14 for such regression-based implementation is shown in FIG. 2. It is commonly called perceptron of linear classifier type. For obtaining the next value to be transmitted to the PID regulator 13 for each of the the k.sub.p-, k.sub.i- and k.sub.d-coefficients, all the measured values for at least some of V.sub.input, I.sub.input, V.sub.output, I.sub.output and V.sub.phase_input, I.sub.phase_input, V.sub.phase_output, I.sub.phase_output for some or all of the phases, and the target output voltage V.sub.target, for all the operating points stored in the FIFO-queue memory set are multiplied with predetermined weights and added together and to predetermined bias. The result of such combination is then inputted as an argument into an activation function dedicated to the k.sub.p-, k.sub.i- or k.sub.d-coefficient. The result of the activation function is the next value for this coefficient to be implemented by the PID regulator 13.

(32) Each calculation structure of such type is a feed-forward neuron, and one separate neuron is dedicated to each of the k.sub.p-, k.sub.i- and k.sub.d-coefficients. In FIG. 2, weights.sub.p and bias.sub.p are the predetermined weights and bias, respectively, that are used for that of the combinations of the measured values and target output voltage which relates to k.sub.p-coefficient. f.sub.p is the activation function for k.sub.p-coefficient. Similar meaning applies separately for weights.sub.i, bias.sub.i, f.sub.i and weights.sub.d, bias.sub.d, f.sub.d with respect to the k.sub.i- and k.sub.d-coefficients. Hidden layers may be added in a known manner within each neuron for determining the k.sub.p-, k.sub.i- and k.sub.d-values in a sharper manner with respect to the operating points. The number of hidden neural layers, the number of operating points which are combined for each k.sub.p-, k.sub.i- and k.sub.d-determination, and also the determination frequency, are to be selected with respect to a balance between computational effort, prediction precision, and special features of each converter application, in particular relating to the load.

(33) In FIG. 2, n is the number of operating points (samples) which are involved for each determination of the k.sub.p-, k.sub.i- and k.sub.d-values, i.e. the number of operating points (samples) in each series for a respective power supply parameter. For the predictor 14 as described before, n is the length of the FIFO-queue memory set. But the memory amount which is thus necessary when n increases and for a multiphase converter may become important. Then, a way to reduce such memory amount is to store at least part of the history information, e.g. the operating points before the last one transmitted by the value-supply system 15 to the predictor 14, directly in the neuron network instead of the entrance FIFO-queue like memory set. Such neural network configuration is known in the art as recurrent neural network. Among such recurrent neural networks, long short-term memories may be preferred because they avoid vanishing or exploding gradients.

(34) The weights and bias for all k.sub.p-, k.sub.i- and k.sub.d-coefficients are the predictor parameters as mentioned in the general part of this description. They are to be provided to the predictor 14 through a preliminary phase called training. Such training is preferably to be achieved by computational hardware/software 40 (see in FIG. 1) which are external to the predictor 14, because of the quite large computer resources that may be necessary for determining the predictor parameters from labelled training data. The computational hardware/software 40 may be provided as a separate computer or be accessed through the cloud. Such configuration for the computational hardware/software 40 that are used for the training phase is advantageous since the computational hardware/software may be shared between a large number of users, thereby allowing computational means that may be expensive to be implemented in a cost-effective manner. Each user can access the computational hardware/software for the initial training phase of the predictor of his power converter, and then his power converter can run for a long duration without requiring the computational means again.

(35) The training phase mainly comprises the following three steps: forming sets of labeled training data, such as each set comprises a series of successive operating points of the converter with associated values for the k.sub.p-, k.sub.i- and k.sub.d-coefficients. In this way, each set of labeled training data describes an operation sequence over time which is possible for the converter, including instant values for the input and output voltages and currents, optionally the phase output voltages and currents, and also for the target output voltage. Desired values for the k.sub.p-, k.sub.i- and k.sub.d-coefficients are associated with each series of successive operating points. In the art, the desired k.sub.p-, k.sub.i- and k.sub.d-values are called labels. The labeled training data may advantageously be selected in a manner appropriate with respect to the application contemplated for the power converter 10, and in particular with respect to its load 30, for obtaining optimized operation of the converter later in its specific application; then the predictor parameters are determined by the computational means 40 using one of known machine-learning processes such as gradient descent, in particular a Newton's method, or a conjugate gradient algorithm, a statistic optimization method, in particular a genetic algorithm, or any process implementing backpropagation, etc; and the predictor parameters are transferred to the predictor 14 for this latter to determine later on the k.sub.p-, k.sub.i- and k.sub.d-values using the predictor parameters. The transfer of the predictor parameters to the predictor 14 may be performed through value transfer or by writing corresponding firmware to be implemented within the predictor 14.

(36) Then, running of the predictor 14 while the converter 10 is supplying the load 30 with DC power results in producing the k.sub.p-, k.sub.i- and k.sub.d-values. The updated k.sub.p-, k.sub.i- and k.sub.d-values are transferred to the PID regulator 13, so that this latter switches from a previously implemented k.sub.p-, k.sub.i- and k.sub.d-value set to the updated one.

(37) FIG. 3 is an example diagram illustrating a PID controller according to embodiments herein.

(38) In this example embodiment, the PID controller 13 receives settings of the power supply coefficients (Kp, Ki, and Kd) from the predictor 14. The PID controller uses the received coefficients to set (control) respective gains of each respective P, I, D path as shown.

(39) FIG. 4 is an example diagram illustrating mapping of current operating settings of a power converter to appropriate control coefficients to achieve a desired control response according to embodiments herein.

(40) As previously discussed, the power converter 10 includes multiple phases 11; the regulator 13 controls the multiple phases 11, converting the input voltage to the output voltage.

(41) In the example embodiment of FIG. 4, the instantiation of predictor 14-1 (such as hardware and/or software) is operative to receive current collected samples of operational settings 210 of the power converter 10. Operational settings 210 are indicated as data set 410-1, data set 410-2, data set 410-3, etc.

(42) Data set 410-1 (such as data stored in multiple FIFO buffers) is a first set of buffered samples obtained at different sample times for each of multiple parameters such as V.sub.input, I.sub.input, etc.

(43) Data set 410-2 (such as data stored in multiple FIFO buffers) is a second set of buffered samples obtained at different sample times for each of multiple parameters such as V.sub.input, I.sub.input, etc.

(44) Data set 410-3 (such as data stored in multiple FIFO buffers) is a third set of buffered samples obtained at different sample times for each of multiple parameters such as V.sub.input, I.sub.input, etc.; and so on.

(45) Thus, each of the prior collected sets of data samples (such as data set 410-1, data set 410-2, etc.) include a respective sequence of multiple data samples for each of multiple parameters (such as V.sub.input, I.sub.input, V.sub.output, I.sub.output, etc.) of the power converter collected over time.

(46) As further shown, the predictor 14 is operative to convert the current collected samples of operational settings 210 of the power converter 10 to appropriate control coefficients 120. In one embodiment, the generated control coefficients 120 is a machine-learned control response assigned to a pattern of previously stored samples of operational settings of the power converter 10 as indicated by the data sets 410.

(47) In one embodiment, the current collected samples of operational settings 210 of the power converter 10 represent current operational conditions of the power converter 10. The previously stored samples of operational settings (such as data set 410-1 indicating a first prior operational condition of power converter 10, data set 410-2 indicating a second prior operational condition of power converter 10, data set 410-3 indicating a third prior operational condition of power converter 10, and so on).

(48) In this example embodiment, based on prior machine learning, each of the different sets of prior detected conditions (operational settings 210) maps to a corresponding appropriate control response.

(49) More specifically, for conditions (such as monitored voltage/current settings) of the power converter 10 as indicated by data set 410-1, the control coefficients 120-1 (such as indicating settings for each of one or more coefficients Kp, Ki, and Kd) indicates a corresponding appropriate control response to control the power converter 10.

(50) For conditions (such as settings) of the power converter 10 as indicated by data set 410-2, the control coefficients 120-2 (such as indicating settings for each of one or more coefficients Kp, Ki, and Kd) indicates a corresponding appropriate control response to control the power converter 10.

(51) For conditions (such as settings) of the power converter 10 as indicated by data set 410-3, the control coefficients 120-3 (such as indicating settings for each of one or more coefficients Kp, Ki, and Kd) indicates a corresponding appropriate control response to control the power converter 10.

(52) For conditions (such as settings) of the power converter 10 as indicated by data set 410-4, the control information 120-4 (such as indicating settings for each of one or more coefficients Kp, Ki, and Kd) indicates a corresponding appropriate control response to control the power converter 10.

(53) In this example embodiment, assume that the current operational settings 210 (for N samples) of the power converter 10 most closely resemble/match the settings as indicated by the data set 410-3. In other words, the current (recently) collected samples of operational settings 210 of the power converter 10 most closely match the pattern of previously stored samples of operational settings of the power converter 10. In such an instance, the predictor 14-1 maps data set 410-3 to the appropriate control response as indicated by the control coefficients 120-3 for selection and application to the PID controller 13.

(54) As previously discussed, in one embodiment, the generated control information 120 (derived from control coefficients 120-3) indicates power supply coefficient settings for the previous operational conditions (associated with data set 410-3). Setting of the one or more PID coefficients in the power converter 10 as specified by the control coefficients 120 maintains the output voltage of the power converter 10 within a desired voltage range.

(55) Subsequent to generating the control 120 (such as selected from control coefficients 120-3), the predictor 14-1 outputs the selected control coefficients 120 to the PID controller 13 or other suitable resource to control the multiple phases.

(56) Accordingly, in one embodiment, the predictor 14-1 is further operative to map the current collected samples of operational settings 210 of the power converter 10 to the previously stored samples of operational settings (such as data set 410-3) of the power converter 10 to identify and select appropriate control coefficients 120-3 for current operational settings 210 of the power supply. As previously discussed, the previously stored samples of operational settings (as indicated by the data set 410-3 are one of multiple sets of previously stored samples of operational settings (data sets 410) of the power converter.

(57) FIG. 5 is an example diagram illustrating mapping of current operating settings of a power converter 10 to multiple sets of control coefficients and derivation of control coefficients from the multiple sets to achieve a desired control response according to embodiments herein.

(58) In this example embodiment, the predictor 14-1 identifies that the current operational settings 210 most closely match both the settings as specified by the data set 410-3 and settings as specified by the data set 410-4. In such an instance, the predictor 14-1 applies interpolation and/or extrapolation techniques to derive control coefficients 120 from the combination of control coefficients 120-3 and control coefficients 120-4.

(59) FIG. 6 is an example diagram illustrating use of logic to derive control information to control a power converter according to embodiments herein.

(60) In this example embodiment, similar to FIG. 3, the processing logic of predictor 14-2 receives current operational settings 210 of the power converter 10 such as stored in buffers 610 and derives control coefficients 120 based on such information.

(61) Buffer 610-1 stores samples of V.sub.input; buffer 610-2 stores samples of I.sub.input; buffer 610-3 stores samples of V.sub.phase output; buffer 610-4 stores samples of I.sub.phase_output; and so on.

(62) Control coefficients 120 indicates settings to apply to the regulator 13 in a manner as previously discussed.

(63) FIG. 7 is an example block diagram of a computer system for implementing any of the operations as previously discussed according to embodiments herein.

(64) Any of the resources (such as predictor 14, regulator 13, etc.) as discussed herein can be configured to include computer processor hardware and/or corresponding executable instructions to carry out the different operations as discussed herein.

(65) As shown, computer system 750 of the present example includes an interconnect 711 that couple computer readable storage media 712 such as a non-transitory type of media (which can be any suitable type of hardware storage medium in which digital information can be stored and retrieved), a processor 713 (computer processor hardware), I/O interface 714, and a communications interface 717.

(66) I/O interface(s) 714 supports connectivity to repository 780 and input resource 792.

(67) Computer readable storage medium 712 can be any hardware storage device such as memory, optical storage, hard drive, floppy disk, etc. In one embodiment, the computer readable storage medium 712 stores instructions and/or data.

(68) As shown, computer readable storage media 712 can be encoded with communication predictor application 140-1 (e.g., including instructions) to carry out any of the operations as discussed herein.

(69) During operation of one embodiment, processor 713 accesses computer readable storage media 712 via the use of interconnect 711 in order to launch, run, execute, interpret or otherwise perform the instructions in predictor application 140-1 stored on computer readable storage medium 712. Execution of the predictor application 140-1 produces predictor process 140 2 to carry out any of the operations and/or processes as discussed herein.

(70) Those skilled in the art will understand that the computer system 750 can include other processes and/or software and hardware components, such as an operating system that controls allocation and use of hardware resources to execute communication management application 140-1.

(71) In accordance with different embodiments, note that computer system may reside in any of various types of devices, including, but not limited to, a mobile computer, a personal computer system, a wireless device, a wireless access point, a base station, phone device, desktop computer, laptop, notebook, netbook computer, mainframe computer system, handheld computer, workstation, network computer, application server, storage device, a consumer electronics device such as a camera, camcorder, set top box, mobile device, video game console, handheld video game device, a peripheral device such as a switch, modem, router, set-top box, content management device, handheld remote control device, any type of computing or electronic device, etc. The computer system 750 may reside at any location or can be included in any suitable resource in any network environment to implement functionality as discussed herein.

(72) Functionality supported by the different resources will now be discussed via the flowchart in FIG. 8. Note that the steps in the flowcharts below can be executed in any suitable order.

(73) FIG. 8 is a flowchart 800 illustrating an example method according to embodiments. Note that there will be some overlap with respect to concepts as discussed above.

(74) In processing operation 810, the predictor 14 receives current samples of operational settings 210 of the power converter 10.

(75) In processing operation 820, the predictor 14 derives a set of power supply coefficients 120 (such as Kp, Ki, and/or Kd) from the current samples of operational settings 210 of the power converter 10, the set of power supply coefficients 120 being a machine-learned control response assigned to a corresponding set of prior samples of operational settings of the power converter 10 to maintain the output voltage within regulation.

(76) In processing operation 830 (such as a sub-operation of processing operation 820), the predictor 14 maps the current samples of operational settings 210 of the power converter 10 to the prior samples of operational settings of the power converter 10 to identify appropriate control coefficients 120 to maintain the output voltage within regulation.

(77) In processing operation 840 (such as an alternative to sub-operation 830), the predictor 14 inputs the current samples of the operational settings 210 to processing of the predictor 14, which is operative to produce the control coefficients 120 from the received settings 210.

(78) In processing operation 850, the predictor 14 outputs the control coefficients 120 to the PID controller 13 to control the multiple phases of the power converter 10.

(79) Although the detailed description has been focused on predictor embodiments suitable for implementing nearest-neighbor or regression-based machine-learning models, one should understand that the invention is not limited to these specific models, and others can be used alternatively. In particular, any regression variant and any sequence based on hidden Markov chains may be used.

(80) One should also understand that the invention applies to any electrical power conversion other than DC-DC, in particular AC-DC power conversion, although the detailed description has been focused on DC-DC power conversion for illustrative purpose.

(81) Finally, one should further understand that the invention applies for any regulator type, without being limited to PID regulators. In each case, the predictor is adapted for providing updated values for the parameters as implemented in the regulator used for producing the power conversion.