GA based predictive functional control for batch processes under actuator faults

• A PFC control using GA optimization is proposed.
• The design is applied to injection velocity control.
• Improved control performance is achieved.

In this article, an improved predictive functional control (PFC) based on genetic algorithm (GA) optimization is proposed for batch processes with partial actuator faults and unknown disturbances. The design consists of two steps. Firstly, based on an expanded state space process model, a new PFC is designed that can regulate both the process state and output tracking error dynamics. Secondly, as knowledge of analytical weighting factors on the process state and output tracking error is not known, a GA is introduced to optimize the weighting factors to achieve the desired closed-loop responses. The performance of the proposed PFC is illustrated through an injection molding process, where injection velocity control is tested and results show that the proposed PFC improves closed-loop performance compared with existing methods.