Data-Driven Iterative Learning Model Predictive Control With Self-Modified Prior Knowledge

Iterative learning model predictive control (ILMPC) has become an excellent data-driven intelligent control strategy for digitized batch manufacturing, featured by the progressive improvement of tracking performance along trials, and the persistent rejection of stochastic disturbance along time. The point-to-point learning mechanism of existing ILMPC generally relies on identical operating conditions along trials to guarantee the integrity and accuracy of historical data. However, the variations of production requirements usually lead to trial-varying operating references and durations, resulting in incomplete and inaccurate historical information for the iterative learning of subsequent trials. To promote the adaptability and flexibility of ILMPCs with unconformable prior information, a data-driven self-modification scheme is originally embedded into ILMPC in this article to transfer the prior knowledge contained in the historical operating data into the form consistent with the condition of each current trial. The control actions are imitated along trials by an adaptive deep neural network (DNN), which is then utilized to generate reference control signals for iterative learning in each trial. For attenuating the influence of the considerable DNN approximation error in early trials with limited data accumulation, the 2-D optimization of ILMPC is performed under a tube control frame to ensure the time-domain bounded stability. Based on the intrinsic recursive feasibility and the guaranteed time-domain stability, the iteration-domain bounded convergence of the developed ILMPC system is theoretically validated. Simulations on the nonlinear injection molding process verify the superiority of the proposed method in adapting to significant changes in operating reference and duration.