Model Predictive Controller Based on a State Space Fuzzy Model

This paper presents the application of a combined control strategy grounded in a basic Model Predictive Control (MPC) structure, but using a Discrete Fuzzy Model (DFM) of the process in the state space domain. In order to overcome the drawback of the non-convex optimization problem generated when a non-linear model is used, two methods of optimization are tested. The first one is the traditional sequential method where the model is solved at each iteration before performing the optimization. The second is the simultaneous method, which integrate the solution of the model into the optimization problem, provided the fulfillment of some conditions in the DFM. A particular DFM that fulfill such conditions is presented. The advantages and disadvantages of the two methods are exposed in accordance with the results of simulation tests. Finally, some conclusions and future research topics are given. Author to whom correspondence must be addressed. Av. L. San Martín (O) 1109, 5400. San Juan. República Argentina.