Terminal constrained robust hybrid iterative learning model predictive control for complex time-delayed batch processes. (February 2023) - Wang, Limin; Zhang, Wangxi; Zhang, Qiyuan; Shi, Huiyuan; Zhang, Ridong; Gao, Furong
Abstract: This work mainly addresses terminal constrained robust hybrid iterative learning model predictive control against time delay and uncertainties in a class of complex batch processes with input and output constraints. In this work, an equivalently novel extended two-dimensional switched system is first constructed to represent the process model by introducing state difference, output error and new relaxation variable information. Then, a hybrid predictive updating controller is proposed and an optimal performance index function including terminal constraints is designed. Under the condition that the switching signal meets certain conditions, the solvable problem of model predictive control is realized by Lyapunov stability theory. Meanwhile, the design scheme of controller parameters is also given. In addition, the robust constraint set is adopted to overcome the disadvantage that the traditional asymptotic stability cannot converge to the origin when it involves disturbances, such that the system state converges to the constraint set and meets its expected value. Finally, the effectiveness of the proposed algorithm is verified by controlling the speed and pressure parameters of the injection molding process. Highlights: The work presents an iterative learning predictive control method with terminal constraints. An equivalent new extended two-dimensional Fornasini–Marchesini (2D-FM) switched system to construct the delay dependent sufficient conditions of the robust iterative learning predictive controller. The robust constraint set is adopted to overcome the disadvantage that the traditional asymptotic stability cannot converge to the origin when it involves disturbance.