| 论文简介 |
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. |
(创新港)
