This paper addresses the genetic algorithm (GA) optimization and the linear quadratic (LQ) structurebased predictive functional control (PFC) for batch processes under non-repetitive unknown disturbances and partialactuator faults. First, by adopting the extended non-minimal state space (ENMSS) model in which the state variablesand the tracking error are united, the new state vector with more degrees is provided for the controller design. Inorder to enhance the ensemble control performance under the PFC structure, GA is adopted for the optimizationof the weighting matrix in the controller. The case study on the injection velocity control in an injection moldingmachine demonstrates the effectiveness of the proposed PFC scheme against various disadvantages.
This paper addresses the genetic algorithm (GA) optimization and the linear quadratic (LQ) structurebased predictive functional control (PFC) for batch processes under non-repetitive unknown disturbances and partialactuator faults. First, by adopting the extended non-minimal state space (ENMSS) model in which the state variablesand the tracking error are united, the new state vector with more degrees is provided for the controller design. Inorder to enhance the ensemble control performance under the PFC structure, GA is adopted for the optimizationof the weighting matrix in the controller. The case study on the injection velocity control in an injection moldingmachine demonstrates the effectiveness of the proposed PFC scheme against various disadvantages.