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| Received:August 31, 2003Revised:March 26, 2004 |
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| Filter-based iterative learning control for linear large-scale industrial processes |
| Xiao' e RUAN, Jianguo WANG, Baiwu WAN |
| (Faculty of Science, Xi' an Jiaotong University, Xi' an Shanxi 710049, China;School of Science, Xi'an University of Architecture and Technology, Xi'an Shaanxi 710055, China;Systems Engineering Institute, Xi' an Jiantong University, Xi' an Shaanxi 710049, China) |
| Abstract: |
| 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. Numerical simulation demonstrates the effectiveness of the proposed strategy, |
| Key words: Iterative learning control Large-scale industrial processes Steady-state optimization Dynamic performance |
