MIQP-Based Iterative Learning Model Predictive Control for Multi-Phase Batch Process

Le résumé fourni par la source

This paper proposes an iterative learning model predictive control(IILMPC) strategy based on mixed-integer quadratic programming (MIQP) to address the phase switching and control issues in multi-phase batch processes. By formulating system dynamics through difference equations along the batch processing direction and utilizing model predictive control(MPC) within the iterative learning control(ILC) framework, the system's state is predicted and phase switching is represented using logical variables. This approach enables the optimization of control performance at a global level. Simulation results validate the effectiveness of the proposed method, particularly exhibiting more smoothly near the switching time.

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