Abstract
Fuzzy predictive control integrates conventional model-based predictive control with techniques from fuzzy multicriteria decision making. The information regarding the fuzzy criteria of the control problem is combined by using a decision function from the fuzzy set-theory. The use of fuzzy criteria in the cost function leads usually to a non-convex optimization problem, which is numerically complex. The numeric optimization problem becomes more tractable by discretizing the control actions, limiting the search of the optimal solution to this space. This paper extends the application of the branch-and-bound optimization technique to predictive control problems with fuzzy cost functions. This approach can reduce significantly the search time, allowing the application of fuzzy predictive control to a broader class of systems, and to real-time control problems. Simulation and real-time results of temperature control in a fan-coil unit show the applicability of the approach.
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