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OPTIMIZING HYBRID DYNAMIC PROCESSES BY EMBEDDING GENETIC ALGORITHMS INTO MPC

Abstract Optimizing the operation of processes with hybrid dynamics is challenging since the discrete dynamics (i.e. abrupt changes of states or inputs) introduces discontinuities with which gradient-based solvers often cannot cope very well. This contribution suggests a scheme that combines model predictive control (MPC) with genetic algorithms and embedded simulation of the hybrid dynamics. As demonstrated for the example of a chemical reactor, the genetic algorithm provides good results even if the prediction horizon includes points of discontinuities of the continuous dynamics.