Robust Multiple Model Adaptive Control using Fuzzy Posterior Probability Generator

The robust adaptive control of dynamic system using multiple models has emerged as a practically available and mathematically attractive method. It can be used to improve transient performance in adaptive control systems with large uncertainty or time varying parameters. The main idea in multiple model controls is to identify the best model of the system at any instant of time and apply the suitable control input to it. In this paper, a new algorithm based fuzzy control scheme called Fuzzy Posterior Probability Generator (FPPG) is proposed to replace Posterior Probability Evaluator (PPE) in robust multiple model adaptive control (RMMAC) architecture. FPPG is proposed to generate the weights for probabilistic weighting of the local controls to from the global signal control. Compensators are designed by the mixed-µ synthesis method so that the robust stability and performance are guaranteed. Local kalman filters are designed to produce residual signals utilized by the FPPG. A simulation example is presented to demonstrate the effectiveness of the proposed method against PPE.

Keywords:

Robust multiple model adaptive control , Fuzzy control , Kalman filter , Fuzzy Posterior Probability Generator