An Iterative Optimization Approach for Fuzzy Predictive Control

This paper proposes an iterative approach in fuzzy model predictive control. When the prediction model is nonlinear or uncertain, non-convex optimization is often encountered which has to be solved by iterative approximation. An alternative is to convert the original issue into a min-max robust MPC problem, where the knowledge of the predictive membership function is not utilized. In this paper, based on the robust MPC approach, we further enhance the model prediction by iteratively applying the optimal control move and state sequences in order to improve the performance. A numerical example is provided to illustrate the effectiveness of the proposed approach.