Iterative learning robust MPC hybrid fault-tolerant control for multi-phase batch processes with asynchronous switching

It is widely known that uncertainties, unknown disturbances, asynchronous switching, and partial actuator faults are the major factors that affect system stability during actual industrial production. For the above problems, a method of iterative learning robust MPC hybrid fault-tolerant control for multi-phase batch processes with asynchronous switching in two-dimensional systems is proposed. Exceptionally, an equivalent extended asynchronous switching fault-tolerant control model, including a synchronous sub-model and an asynchronous sub-model, is built. Then, Lyapunov theory, switching system theory, and so on are used as the theoretical basis, and the sufficient conditions to guarantee the stable operation of the system are given. Combined with the given conditions, the control law gain, the shortest running time, and the longest running time are solved in real time to eliminate the asynchronous switching situation problem. The state deviations of the system are corrected in time by avoiding the accumulation of the system state deviations over time, thus improving the control performance of the system. Meanwhile, by combining real-time control law gains with information about the batch direction, the method can significantly reduce the learning period of the controller and provide better control performance along the batch direction. Finally, the feasibility of the proposed method is verified with simulation experiments of the injection molding process.