A comparative study of fuzzy and conventional criteria in model-based predictive control

Fuzzy predictive control integrates conventional model-based predictive control with techniques from fuzzy multicriteria decision making, and translates the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the control problem is combined by using a decision function from the fuzzy set theory. This paper investigates the use of fuzzy decision making in predictive control and compares the results to those obtained from conventional model-based predictive control. Experiments on a nonminimum phase, unstable linear system and air conditioning system with nonlinear dynamics are studied. It is shown that the performance of the model-based predictive controller can be improved by the use of fuzzy goals and criteria with fuzzy decision-making techniques