The Iterative Learning Control for Bilinear Industrial Process Control Systems under Steady-state Optimization
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发布时间：2025-04-30
发布时间：2025-04-30
论文名称：The Iterative Learning Control for Bilinear Industrial Process Control Systems under Steady-state Optimization
发表刊物：Journal of Systems Science and Information
摘要：In this paper, based on the hierarchical control structure of industrial processes steady-state optimization and the iterative learning control law for linear industrial process control systems, the dynamic description of the bilinear processes control system is derived and to which the iterative learning control is applied. The weighted closed-loop PD-type and open-loop PD-type iterative learning control algorithms are suggested respectively. The definitions of -reachability of desired trajectories and -convergence of the iterative learning control algorithms are defined. By means of Bellman-Gronwall inequality and -norm theory, the convergence of the algorithms is proved. The simulation results and their comparison of the closed-loop algorithm with the open-loop algorithm are shown. The numerical simulation results show that the iterative learning control can remarkably improve the dynamic performance of bilinear industrial control systems. This indicates the effectiveness of the algorithms.
合写作者：Ruan Xiaoe , Wan Baiwu
卷号：vol.29,no.1
页面范围：125-129
是否译文：否
发表时间：2003-03-10