An adaptive asynchronous switching control strategy based on event-triggered mechanism and minimum average dwell time for multi-phase batch processes

A novel two-dimensional adaptive asynchronous switching control strategy is proposed for multi-phase batch processes. It integrates the event-triggered mechanism with the minimum average dwell time to address the mismatch between control inputs and system states caused by asynchronous subsystem switching. In contrast to conventional fixed-threshold approaches, the proposed event-triggered scheme adaptively adjusts its triggering sensitivity via subsystem-dependent weighted norms. It also evaluates the incremental variations of tracking error and system state to determine the necessity of updates. The design enables rapid response to abrupt dynamic changes during switching transitions while suppressing redundant updates during steady-state operation. An incremental model is constructed to capture the dynamic characteristics of each phase. State feedback control is implemented along the time axis, and iterative learning control is applied along the batch axis, enabling fast convergence and accurate trajectory tracking. Furthermore, the adaptive update of the control gain matrix enhances robustness under varying phase dynamics. Simulation results verify the effectiveness of the proposed method in multi-phase asynchronous control scenarios.