Hierarchical Adaptive Control of Rapidly Time-Varying Systems Using Multiple Models

Abstract Adaptive systems that continuously monitor their own performance and adjust their control strategies to improve it have been studied for more than 50 years. The theory of such systems when the plants (or processes) to be controlled are linear and time-invariant is currently well understood. Numerous methods currently exist to achieve a satisfactory and robust response when the uncertainty in the system is small. During the past 3 decades numerous attempts have been made by workers in the field to extend the methods to systems with larger uncertainties. During this period the author and his colleagues have attempted to use a general method based on multiple models to control rapidly time-varying plants. More specifically, they have proposed four distinct methods based on (1) switching, (2) switching and tuning, (3) interactive/evolutionary adaptation, and (4) second-level adaptation. In this chapter, which is tutorial in nature, the four methods are critically examined. Work currently in progress which attempts to combine them using a hierarchical approach is described. Since many of the problems considered were formulated only recently, they are open-ended and hopefully will be of interest to a wide audience, including both beginners and experts.