Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10226039 | Journal of the Franklin Institute | 2018 | 25 Pages |
Abstract
This paper considers the adaptive iterative learning control (ILC) for continuous-time parametric nonlinear systems with partial structure information under iteration-varying trial length environments. In particular, two types of partial structure information are taken into account. The first type is that the parametric system uncertainty can be separated as a combination of time-invariant and time-varying part. The second type is that the parametric system uncertainty mainly contains time-invariant part, whereas the designed algorithm is expected to deal with certain unknown time-varying uncertainties. A mixing-type adaptive learning scheme and a hybrid-type differential-difference learning scheme are proposed for the two types of partial structure information cases, respectively. The convergence analysis under iteration-varying trial length environments is strictly derived based on a novel composite energy function. Illustrative simulations are provided to verify the effectiveness of the proposed schemes.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Chun Zeng, Dong Shen, JinRong Wang,