Genetic-Algorithm-Optimization-Based Infinite Horizon Linear Quadratic Control for Injection Molding Batch Processes with Uncertainty

A new genetic-algorithm (GA)-based infinite horizon linear quadratic (IHLQ) control method is developed for injection molding batch processes under partial actuator faults and unknown disturbances. To obtain improved system performance, an extended state space model, which combines the process state and output error information, has been adopted, where extra tuning freedom is gained for the corresponding control system design by adjusting the extended weighting matrix. However, no specified rules are established for the choices of these weighting coefficients. To cope with such situations, GA is employed to optimize these weighting factors in this paper. Furthermore, the robust stability of the control system, which can be regarded as the process under uncertainty, is addressed. Finally, the validity of the proposed approach is tested on the injection velocity regulation process.