Robust Adaptive Generalized Predictive Control Based on Takagi-Sugeno Fuzzy Model.

Predictive adaptive algorithms are powerful tools for controlling time varying systems. In particular, the Adaptive Generalized Predictive Control is widely used in different plants or processes. Nevertheless, a lack of robustness is often observed especially in case of large uncertainties in the model and in the presence of strong nonlinearities. Better solutions have been proposed using fuzzy modelling. In this paper, a direct adaptive version of the generalized predictive controller is developed. This allows an automatic online and real-time adjustment of local controller settings in order to maintain a proper level of performance. The contributions presented in this paper lie in the merging of local controllers using a fuzzy technique in order to produce a global controller using on-line parameters identification by a recursive lest squares type strategy. The stability of the closed-loop control system is demonstrated through the Lyapunov Stability Theory. Highly improved results are confirmed by simulation on an example of the commonly used Continuous Stirred Tank Reactor system. [ABSTRACT FROM AUTHOR]