.png){kind=icon}
.png){kind=icon}
Return
High-order internal model-based iterative learning control design for nonlinear distributed parameter systems
.png){kind=icon}
PRE
AI
DOI：10.1002/rnc.5052.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This article deals with the problem of iterative learning control algorithm for a class of nonlinear parabolic distributed parameter systems (DPSs) with iteration-varying desired trajectories. Here, the variation of the desired trajectories in the iteration domain is described by a high-order internal model. According to the characteristics of the systems, the high-order internal model-based P-type learning algorithm is constructed for such nonlinear DPSs, and furthermore, the corresponding convergence theorem of the presented algorithm is established. It is shown that the output trajectory can converge to the desired trajectory in the sense of(L-2,lambda)-norm along the iteration axis within arbitrarily small error. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.
Keywords:
iterative learning control
iteration-varying desired trajectory
high-order internal model
nonlinear parabolic distributed parameter systems
P-type learning algorithm
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.2
Papers:
6.9K
Citations:
1.4W