US 2021/0341895 A1 · PID CONTROLLER AUTOTUNER USING MACHINE LEARNING APPROACHES

Abstract

An approach for auto tuning a PID controller that may incorporate determining set-points for controlled variables, obtaining controlled variables from a controlled process module, taking a difference between the set-points and the controlled variable as inputs to a proportional, integral and derivative (PID) controller, calculating a loss or reward from the difference between the controlled variables and the set-points, and achieving set-point tracking. The loss or reward needs to be an increasing or decreasing function of a control error value. Also incorporated may be adding loss or reward components based on controlled variables time difference or control action time difference, which may effect a self-tuned performance of the PID controller.

Claims20

Dependent claims

A PID controller system comprising:

a proportional, integral and derivative (PID) controller;
a machine learning module having an output connection to the PID controller;
and
a controlled process module that provides controlled variables to the PID controller via a differentiator that also takes in set-points for the controlled variables and the differentiator outputs a control error to the PID controller, wherein the control error is a difference between the set-points and the controlled variables;
and
wherein:
the controlled variables and set-points for the controlled variables are input to the machine learning module;
and
the machine learning module calculates an instantaneous loss or reward which is an increasing or decreasing, respectively, function of the control error.

6 more elements

Dependencies are inferred from the claim text.

Pid controller autotuner using machine learning approaches

Life of this patent

Pid controller autotuner using machine learning approaches

Life of this patent

Abstract

Claims20