| 论文简介 |
In the procedure of steady-state hierarchical optimization with feedback for a large-scale industrial process, it is usual that a sequence of step set-point changes are carried out and used by the decision-making units while searching the eventual optimum. In this case, the real process experiences a form of disturbances around its operating set-point. In order to improve the dynamic performance of transient responses for such a large-scale system driven by the set-point changes, an open-loop PD-type iterative learning control strategy is explored in this paper, by considering the different magnitudes of the controller’s step set-point changes sequence. Utilizing the HausdorffYoung inequality of convolution integral, the convergence of the algorithm is derived in the sense of Lebesgue-P norm. Furthermore, the extended higher order iterative learning control rule is developed and the convergence is analyzed. Simulation results illustrate that the proposed iterative learning control strategies can remarkably improve the dynamic performance such as decreasing the overshoot, accelerating the transient response and shortening the settling time, etc. |
(创新港)
