Nonlinear-approximator-based automatic tuner

Abstract

An automatic tuner for control systems that produces, as output values, parameters of an arbitrary controller. The controller is in a control loop so that its output effects changes in actuators and regulates a physical process. The controller has either linear or nonlinear controller components, or a combination of both. The tuner has a nonlinear approximator that has been optimized off-line. The off-line optimization is done without supervised learning so that desired outputs of the nonlinear approximator do not need to be available, and separate optimization to generate the desired outputs is not necessary. The off-line optimization can also rely on arbitrary criteria. Such optimization ensures robustness of generated controller parameters so that the input process characteristics do not need to be highly accurate. The inputs to the nonlinear approximator consist of two sets of input parameters, either of which may be empty. A first set of input parameters can relate to process characteristics. A second set of input parameters can relate to desired closed-loop system behavior. The output values may be proportional and/or integral and/or derivative gains for PID-like controllers, or otherwise be parameters for delay-compensation controllers, controllers that consist of lead-lag terms in combination with PID controllers, higher-order linear controllers, or nonlinear controllers of predetermined structure. The nonlinear approximator may be implemented as a compositional sigmoidal mapping, a multilayer perception structure, a fuzzy logic model, a radial basis function network, a polynomial expansion, or other parametrized nonlinear structure.