Robust Model Predictive Control for Multi-phase Batch Processes with Asynchronous Switching

A robust asynchronous switching model predictive controller is designed for multi-phase batch processes with uncertainties, unknown disturbances and time-varying set-point. Firstly, in view of the influence of time-varying set-point and disturbances, an asynchronous switching model with stable and unstable cases is established. Based on the switching model, a robust asynchronous switching model predictive control law is designed. Secondly, by using relevant theories and methods, the sufficient conditions with the form of linear matrix inequality (LMI) are given to ensure that the multi-phase batch processes are asymptotically stable at each phase and exponentially stable at each batch. Then, these LMI conditions are solved online to obtain the control gain of each phase, the shortest running time of each stable case, and the longest running time of each unstable case. Finally, the effectiveness and feasibility of the proposed method are verified by taking the injection molding process as an example.