Algorithms for iterative learning control of nonlinear plants employing time-variant system descriptions

Abstract

Deals with the design of iterative learning controllers (ILC) based on extended state space models for nonlinear cyclic process control. In order to design a suitable learning operator, knowledge about the plant's dynamical behaviour is needed which implies that a system model has to be set up. It is expedient to acquire a state space model of the plant using identification methods. Here we deal especially with the case, that a linear model represents system dynamics inadequately. We start with a nonlinear model and linearize the system along the current trajectory, thus obtaining a linear time variant model. Using this as the basis, we develop methods for identification and control of the nonlinear process. Experimental results show that a good system model is also useful to perform a pre-training for the ILC; this is especially interesting in case large deviations from a desired system output trajectory must be avoided. The presented algorithms have been implemented and tested experimentally with a real-life nonlinear processing plant.