Filter-based Iterative Learning Control for Linear Large-scale Industrial Processes
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发布时间：2025-04-30
发布时间：2025-04-30
论文名称：Filter-based Iterative Learning Control for Linear Large-scale Industrial Processes
发表刊物：Journal of Control Theory and Applications
摘要：In the procedure of the steady-state hierarchical optimization with feedback for large-scale industrial processes, a sequence of set-point changes with different magnitudes is carried out from the optimization layer. For improvement the dynamic performance of transient response driven by the set-point changes, a filter-based iterative learning control strategy is proposed in this paper. In the proposed updating law, a local-symmetric-integral operator is adopted for eliminating the measurement noise of output information, a set of desired trajectories are specified according to the set-point changes sequence, the current control input is iteratively achieved by utilizing smoothed output error to modify its control input at previous iteration, to which the amplified coefficients relating to the different magnitudes of set-point changes are introduced. The convergence of the algorithm is conducted by incorporating frequency-domain technique into time-domain analysis. Numerical simulation demonstrates the effectiveness of the proposed strategy.
合写作者：Xiaoe Ruan, Jianguo Wang, Baiwu Wan
卷号：vol.2, no.2
页面范围：149-154
是否译文：否
发表时间：2004-05-10