Discrete Forward-Backward Fuzzy Predictive Control

An extension of the model predictive control philosophy to the field of fuzzy control design is discussed. The main goal is to bring together the best features from both techniques. The basic idea is to divide the initial optimization problem in a set of recursive optimization subproblems or decision stages. Each subproblem is raised as a fuzzy LQR design where the goal is to define the set of feedback gains of a fuzzy Parallel Distributed Compensator (PDC) that minimizes the function cost using Linear Matrix Inequalities (LMIs). Therefore, the global controller is a set of PDC controllers that satisfies the Bellman optimality principle, minimizing the cost function both locally and globally, and guarantees stability and satisfies the control action constraints.