Filter-based iterative learning control for linear large-scale industrial processes
Xiao'e RUAN
Jianguo WANG
Baiwu WAN
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 on the optimization layer. To improve the dynamic performance of transient response driven by the set-point changes, a filter-based iterative learning control strategy is proposed. 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 related 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. Numnerical simulation demonstrates the effectiveness of the proposed strategy.
机标关键词：
分类号：O1(数学)
资助基金：国家自然科学基金(60274055)
论文发表日期：2004-01-01
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：6( 149-154 )
英文信息