In this paper we describe the design of hybrid fuzzy predictive control based on a genetic algorithm (GA). We also present a simulation test of the proposed algorithm and a comparison with two hybrid predictive control methods: Explicit Enumeration and Branch and Bound (BB). The experiments involved controlling the temperature of a batch reactor by using two on/off input valves and a discrete-position mixing valve. The GA-hybrid predictive control strategy proved to be a suitable method for the control of hybrid systems, giving similar performance to that of typical hybrid predictive control strategies and a significant saving with respect to the computation time.@en

The paper deals with model predictive control (MPC) of nonlinear hybrid systems with discrete inputs. It is often required to take into account the hybrid and/or nonlinear nature of real systems, therefore, a hybrid fuzzy model is used for MPC in the paper. Two approaches that are suitable for MPC of nonlinear hybrid systems with discrete inputs are compared on a batch reactor example: a branch & bound and a genetic algorithm. We have established that both algorithms are suitable for controlling such systems. The main advantages of the genetic algorithm are boundedness of computational time in one step and whole computation-e±ciency, whereas the main drawbacks are its inherent sub-optimality and the need for suitably tuned parameters. On the other hand, the branch & bound approach does not require parameter tuning and using a suitable cost function provides optimal results in considerably less time than an explicit enumeration method.@en