Application of indirect adaptive model predictive control supervised by fuzzy logic to a diesel generator

Physical system can be described by many models according to the operating points. The operating point change affects sometime negatively the output plant behavior. Some strategies such as model predictive control propose to estimate online the plant model parameters. Because it does not ensure always good results, we suggest reconfiguring the initial tuning parameters of model predictive control. One adds to the model adaptation a fuzzy logic supervisor which performs the second adaptation regarding measurable performance criteria. Finally, both strategies are applied on a diesel generator taking account of constraints on the control signal and its variation to highlight the relevance of our approach.