Optimal model distribution in multi model adaptive control with switching

Abstract

Multi model adaptive control is an emerging field that has proven to be successful in mitigating limitations of classical adaptive control by providing more quicker and faster adaptation. In this method instead of one single parameter varying controller, multiple fixed parameter controllers pertaining to different operation regimes are utilized. A variant of this technique known as the multi model unfalsfied adaptive control is a recently proposed method. This methodology although promising needs further systematic analysis regarding the determination of the minimum number of controllers required and their corresponding locations (in the uncertain parameter space) to address stability and performance issues. This research work presents the basics of Adaptive Control with Multiple Models, introduces the problem of optimal problem distribution of the multiple models, and finally demonstrates a novel optimization based methodology to determine the number of controllers and their optimal locations for a given uncertainty.