| 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. |
