Iterative learning control (ILC) system is essentially a special feedback control system with two-dimensional (2D) dynamics that can be designed and optimized under the framework of 2D system theories. Motivated by this viewpoint, it is proposed in this paper to describe a batch process with 2D dynamics directly using a 2D controlled auto-regressive moving average model, and then, design a 2D feedback controller, referred to as two-dimensional generalized predictive control, in the framework of model predictive control. The proposed design method naturally results in an ILC algorithm when the process is assumed as a one dimensional process performing a given task repetitively and guarantees the better control performance along cycle by utilizing the cycle-wise dynamics of the process. The proposed control scheme is the further generalization and extension of the two-dimensional generalized predictive iterative learning control scheme which has been developed in the previous works. It solves the problem in some degree that conventional ILC cannot guarantee the convergence when there are non-repeatable dynamics in the processes and/or in desired trajectories. The effectiveness and the applicability are illustrated by the comparisons of the simulation results and the experimental results on packing pressure control of the injection molding process.

In this paper, an optimal iterative learning control (ILC) algorithm based on a time-parametrized linear time-varying (LTV) model for batch processes is proposed. Utilizing the repetitive nature of batch processes, a time-parametrized LTV model is used to represent the nonlinear behavior, with its consistence and variance properties established. Furthermore, an optimal ILC algorithm based on the time-parametrized LTV model is developed, and its convergence property is analyzed. Simulations have demonstrated the effectiveness and excellent performance of the proposed method.

In this paper, a robust iterative learning control (ILC) designed through a linear matrix inequality (LMI) approach is proposed first, based on the worst-case performance index with ellipsoidal uncertainty and polytopic uncertainty, respectively. Since the design based on worst-case performance index is too conservative, a novel ILC design based on nominal performance index is further proposed, and its robust convergence properties are proven. The latter can give better performance when the nominal model is close to the true process. Simulations have demonstrated the effectiveness and excellent performance of the proposed methods.

As a major polymer processing technique, polymer extrusion is a continuous process, during which material properties, machine variables, and process variables interact with each other to determine the final product quality. Precise control of key process variables such as barrel temperatures and melt pressure are crucial to ensure a good product quality in the extrusion process. In this paper, the overall extruder control system is constructed by two independent control loops, a single-input–single-output (SISO) control of the melt pressure at die output, and a multiinput–multioutput (MIMO) control of the barrel temperatures. The dynamic behaviors of melt pressure and barrel temperatures were analyzed first. The characteristics of the melt pressure dynamics were as follows: nonlinear and time-varying while the extruder barrel temperatures were nonlinear, slow response, and different zones were highly coupled. Advanced control algorithms were adopted to control these key variables. Experimental results demonstrate the fast response, near-zero overshoot, and precise tracking performance of the proposed control strategies. The robustness of the entire control system was verified through different operating conditions including materials and set points. Ultimately the performance of the entire control system was verified by product quality. The product quality improved significantly with the proposed controller.