Branch-and-bound optimization in predictive control with fuzzy decision functions

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 not tractable. The numeric optimization problem becomes more tractable by discretizing the control actions, limiting the search of the optimal solution to this space. The computational complexity of an enumerative search, however, remains exponential. The application of branch-and-bound optimization to control problems with fuzzy cost functions, can reduce significantly the search time, allowing the application of fuzzy predictive control to a much broader class of systems. An application to the simulation of an air conditioning system is presented in the paper. Copyright (C) 1998 IFAC.