Robust Iterative Learning Predictive Asynchronous Switching Control with Disturbance Input for Multi-phase Batch Processes

Aiming at a series of multi-phase batch processes with unknown disturbances and asynchronous switching, a two-dimensional robust iterative learning predictive asynchronous switching control with disturbance input is proposed. In contrast to previous robust control methods for handling disturbances, disturbances are converted into a max-min optimization problem by leveraging the established max-min performance index in this study. Based on this, a control law for disturbance inputs is designed, which enables the system to remain stable even when subjected to significant disturbances. Moreover, compared with the traditional iterative learning control methods, the real-time optimized gains of the control laws observably reduce the learning time and make the control process more precise and efficient. Simultaneously, by conducting the exponential stability analysis of the system, the matching switching period and the delay time period are obtained, which allows for the provision of advanced switching signals to avoid asynchronous switching scenarios. Furthermore, a robustness analysis method is proposed for the system operating under adverse conditions, and a robust stability criterion is provided to ensure the robustness of the system. The injection molding process is implemented as a research object to validate the practicality and efficacy of the devised approach.