Optimal input filters for iterative learning control systems with
additive noises, random delays and data dropouts in both channels
- Lixun Huang
, - Lijun Sun,
- Tao Wang,
- Weihua Liu,
- Zhe Zhang,
- Qiuwen Zhang
Abstract
In wireless networked iterative learning control systems, the controller
is separated from the plant, and additive noises, random delays and data
dropouts arise in both sensor-to-controller and controller-to-actuator
channels. In order to guarantee the convergence performance of such
systems with the effect of these uncertainties, an input filter is
designed based on a proportional iterative learning controller, so that
updated inputs can be filtered at the actuator side. Specifically, two
data transmission processes are first developed to describe the mix of
those uncertainties in both channels by Bernoulli and Gaussian
distributed variables with known distributions. Based on state
augmentation, the two data transmission processes are further combined
with the iterative learning process of controllers to build a unified
filtering model. According to this unified model, an optimal filter is
designed via the projection theory and implemented at the actuator side
to filter the updated inputs in iteration domain. Moreover, the
convergence performance of the filtering error covariance matrix is
proved theoretically. Finally, some numerical results are given to
illustrate the effectiveness of the proposed method.22 Jan 2021Submitted to Mathematical Methods in the Applied Sciences 23 Jan 2021Submission Checks Completed
23 Jan 2021Assigned to Editor
29 Jan 2021Reviewer(s) Assigned
13 Aug 2021Review(s) Completed, Editorial Evaluation Pending
20 Sep 2021Editorial Decision: Revise Major
12 Nov 20211st Revision Received
14 Nov 2021Submission Checks Completed
14 Nov 2021Assigned to Editor
14 Nov 2021Reviewer(s) Assigned
14 Nov 2021Review(s) Completed, Editorial Evaluation Pending
17 Nov 2021Editorial Decision: Accept
28 Dec 2021Published in Mathematical Methods in the Applied Sciences. 10.1002/mma.8040