西安交通大学教师主页管理系统阮小娥 The Iterative Learning Control for Bilinear Industrial Process Control Systems under Steady-state Optimization Home

The Iterative Learning Control for Bilinear Industrial Process Control Systems under Steady-state Optimization

Release Time:2025-04-30

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Title of Paper:: The Iterative Learning Control for Bilinear Industrial Process Control Systems under Steady-state Optimization

Summary:: 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.