This paper deals with the problem of iterative learning control for a class of linear continuous-time switched systems in the presence of a fixed initial shift. Here, the considered switched systems are operated durin...This paper deals with the problem of iterative learning control for a class of linear continuous-time switched systems in the presence of a fixed initial shift. Here, the considered switched systems are operated during a finite time interval repetitively. According to the characteristics of the systems, a PD-type learning scheme is proposed for such switched systems with arbitrary switching rules, and the corresponding output limiting trajectories under the action of the PD-type learning scheme are given. Based on the contraction mapping method, it is shown that this scheme can guarantee the outputs of the systems converge uniformly to the output limiting trajectories of the systems over the whole time interval. Furthermore, the initial rectifying strategies are applied to the systems for eliminating the effect of the fixed initial shift. When the learning scheme is applied to the systems, the outputs of the systems can converge to the desired reference trajectories over a pre-specified interval. Finally, simulation examples illustrate the effectiveness of the proposed method.展开更多
基金The NSF(11371013)of Chinathe Research Innovation Program(SKCX17 032)for Graduate Students
文摘This paper deals with the problem of iterative learning control for a class of linear continuous-time switched systems in the presence of a fixed initial shift. Here, the considered switched systems are operated during a finite time interval repetitively. According to the characteristics of the systems, a PD-type learning scheme is proposed for such switched systems with arbitrary switching rules, and the corresponding output limiting trajectories under the action of the PD-type learning scheme are given. Based on the contraction mapping method, it is shown that this scheme can guarantee the outputs of the systems converge uniformly to the output limiting trajectories of the systems over the whole time interval. Furthermore, the initial rectifying strategies are applied to the systems for eliminating the effect of the fixed initial shift. When the learning scheme is applied to the systems, the outputs of the systems can converge to the desired reference trajectories over a pre-specified interval. Finally, simulation examples illustrate the effectiveness of the proposed method.
