Weighted criteria in multivariable fuzzy predictive control

Model predictive control (MPC) is a well-known control technique, which has been applied to complex and nonlinear processes. In order to incorporate fuzzy goals and constraints in model predictive control, MPC have recently been integrated with fuzzy decision making. Conventionally, the fuzzy optimization problem in such a setting is defined as the simultaneous satisfaction of the constraints and the goals. This paper proposes an extension of this model for satisfying the problem constraints and the goals, where preference for different constraints and goals can be specified by the decision-maker. The difference in the preference for the constraints is represented by a set of associated weight factors. Simultaneous weighted satisfaction of various criteria is modeled by using the weighted extensions of (Archimedean) fuzzy t-norms. The weighted satisfaction of the problem constraints and goals are demonstrated by using a multivariable process. The simulation of a gantry crane system is used as case study.