Adaptive Switching Control Applied to Multivariable Systems

PhD Thesis by Michael Change, 1996

Abstract

In this thesis, a family of adaptive control problems is examined and solved using robust self-tuning switching controllers. The motivation for using this type of controller is that, often in practise, no suitable mathematical model of the system to be controlled is available; conventional methods of adaptive controller design generally require specific a priori plant information (e.g. it may have to be known if the plant is minimum phase), and thus cannot be implemented if such a knowledge is not known.

In contrast, this thesis shall generally assume that very little a priori plant information is known -- the main assumption being that the plant can be modelled by a finite dimensional linear time invariant (LTI) system. More specifically, for the adaptive control problem of a family of not necessarily strictly proper multi-input multi-output (MIMO) plants, a switching mechanism which requires less a priori system information than previously considered is proposed. Utilizing this framework, various new self-tuning controllers then are presented, which solve the adaptive stabilization problem and the robust servomechanism problem for potentially unknown MIMO systems.

The proposed controllers appear to be quite attractive in their overall improved tuning transient response when compared with earlier results. Real-time experimental results of one particular class of switching controllers when applied to a multivariable hydraulic apparatus are presented, and illustrate the feasibility of applying such adaptive controllers to industrial process control problems.