The aim of the invention is rapid automatic tuning the parameters of a digital proportional-integral-derivative (PID) controller by analog feedback of an actual value for automation of technological processes with programmable logic controllers (PLCs).

The proposed invention is based on the use of nine tuning equations derived by reverse engineering of a PID controller.

Adjusting the PID controller parameters K.sub.p, K.sub.i and K.sub.d is performed in a closed control loop with negative feedback separately in time, i.e. independently of each other in iteration steps k for K.sub.p, m for K.sub.i and n for K.sub.d (see FIG. 1).

The adaptive tuning method is compact, independent of other methods and algorithms, mathematically balanced (i.e. minimal computational resource requirements), and easy to implement.

Setting up a PID controller by this method does not require a preliminary evaluation of a controlled system and the creation of its mathematical model. This implies its universal applicability.

A method of compressing signal data in an intelligent programmable logic controller includes the controller creating a process image area in a volatile computer-readable storage medium operably coupled to the controller. The intelligent programmable logic controller updates the process image area with contents comprising signal data associated with a production unit and applies a compression algorithm to the contents to generate compressed signal data. The compressed signal data is stored on a non-volatile computer-readable storage medium operably coupled to the controller. The controller annotates the signal data with automation system context information to generate contextualized data. Next, the controller performs a statistical comparison of the signal data and the compressed signal data to generate statistical comparison results. Then, one or more compression parameters used by the compression algorithm may be automatically adjusted based on at least one of the statistical comparison and the contextualized data.

A control parameter computation method includes, by a processor, identifying, for each time segment, a plurality of mathematical models of different structures. The method also includes, computing an adaptiveness representing a level of adaptation between a time series of the computed control variable predicted values and time series data of the control variable in the paired data corresponding to the time segments different from the time segment employed in the identification. The method also includes, selecting, as a mathematical model for the paired data of the time segment. The method also includes, configuring a PID controller, generating a transfer function represented by a product of the PID controller and the selected mathematical model, and generating a function representing a gain margin of the transfer function and a function representing a phase margin of the transfer function. The method also includes computing a parameter Lambda.

The present disclosure relates to a proportional, integral, derivative (PID) control system for controlling a cooling component of a cooling unit. The system may make use of a PID actuator position controller, a memory in communication with the PID actuator position controller, and a plurality of look-up tables. The look-up tables may be stored in the memory and may set forth different proportional P, integral (I) and derivative (D) gains based on an operating variable associated with operation of the cooling component of the cooling unit. The PID actuator position controller uses the lookup tables together with determination of projected data and historical data, to adjust at least one of the P, I and D gains in real time.

An industrial controller can perform multi-dimensional optimization locally using the controller's native hardware and processing. An optimization algorithm is encoded on the industrial controller in a language understandable and executable by the controller (e.g., IEC61131-3). The optimization algorithm is adapted for the scan-based processing performed by industrial controllers rather than sequential processing, thereby allowing the optimization algorithm to be executed by the industrial controller as part of the controller's control program execution. A control program development system allows a user to add and configure the optimization algorithm as an instruction within the controller's control program. The instruction's configurable parameters allow the user to submit constraints and cost functions for the algorithm. During runtime, the controller executes this optimization instruction in accordance with the optimization parameters submitted by the user during development, using values of specified data tags as inputs and outputs for the algorithm.

A control parameter computation method includes, by a processor, identifying, for each time segment, a plurality of mathematical models of different structures. The method also includes, computing an adaptiveness representing a level of adaptation between a time series of the computed control variable predicted values and time series data of the control variable in the paired data corresponding to the time segments different from the time segment employed in the identification. The method also includes, selecting, as a mathematical model for the paired data of the time segment. The method also includes, configuring a PID controller, generating a transfer function represented by a product of the PID controller and the selected mathematical model, and generating a function representing a gain margin of the transfer function and a function representing a phase margin of the transfer function. The method also includes computing a parameter Lambda.

A method of compressing signal data in an intelligent programmable logic controller includes the controller creating a process image area in a volatile computer-readable storage medium operably coupled to the controller. The intelligent programmable logic controller updates the process image area with contents comprising signal data associated with a production unit and applies a compression algorithm to the contents to generate compressed signal data. The compressed signal data is stored on a non-volatile computer-readable storage medium operably coupled to the controller. The controller annotates the signal data with automation system context information to generate contextualized data. Next, the controller performs a statistical comparison of the signal data and the compressed signal data to generate statistical comparison results. Then, one or more compression parameters used by the compression algorithm may be automatically adjusted based on at least one of the statistical comparison and the contextualized data.

The present disclosure relates to a proportional, integral, derivative (PID) control system for controlling a cooling component of a cooling unit. The system may make use of a PID actuator position controller, a memory in communication with the PID actuator position controller, and a plurality of look-up tables. The look-up tables may be stored in the memory and may set forth different proportional P, integral (I) and derivative (D) gains based on an operating variable associated with operation of the cooling component of the cooling unit. The PID actuator position controller uses the lookup tables together with determination of projected data and historical data, to adjust at least one of the P, I and D gains in real time.