RPI-Based Robust Fault-Tolerant Predictive Asynchronous Switching Control with Disturbance Input for Multi-Phase Batch Processes

A robust fault-tolerant predictive asynchronous switching control method based on robust positively invariant sets is proposed for multi-phase batch processes subject to actuator faults, unknown disturbances, and asynchronous switching. To attenuate the effect of unknown disturbances, a min–max performance index is constructed, by which the robust control problem is formulated as a min–max optimization problem under worst-case disturbance conditions. To improve fault tolerance, robust positively invariant sets are introduced into the controller design so that the system states can remain within a constraint-satisfying feasible region under admissible actuator faults. Moreover, an online pre-switching mechanism is developed to address the phase mismatch between the system phase and controller. By updating the switching timing according to the real-time operating state, the controller can be adjusted to the corresponding control law before the system enters the next phase, thereby reducing the mismatched interval and suppressing state deviation. A case study on the injection and holding phases of the injection molding process shows that the proposed method improves tracking accuracy and operational smoothness under actuator faults, unknown disturbances, and asynchronous switching, demonstrating its effectiveness and applicability.