Rule predictive control and model predictive control strategies for Recurrent Fuzzy Systems

Recurrent Fuzzy Systems allow for an approximate modeling of system dynamics based on expert knowledge or measured data. In this paper, the applicability of model predictive control strategies for control of these dynamic fuzzy systems is considered. It is shown that each of the different forms for representation of the system dynamics leads to a specific model predictive control strategy. The main result is the proposition of an explicit model predictive control strategy based on the rule base representation, outperforming existing control strategies in terms of online computation time. The performance of all control strategies is also illustrated and compared by means of a bio reactor example.