Iterative Learning Control for Locally Lipschitz Nonlinear Fractional-order Multi-agent Systems

Luo, Dahui; Wang, JinRong; Shen, Dong

doi:10.1016/j.jfranklin.2020.04.032

Cited by 27 publications

(21 citation statements)

References 34 publications

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“…24 Furthermore, the PI 𝛽 -type and PD 𝛼 -type ILC law with initial state learning mechanism for FOMAS were introduced. 25 However, all above convergence conditions are fractional order independent. Thus, the stability analysis and controller synthesis may be conservative.…”

Section: Introductionmentioning

confidence: 99%

Finite difference based iterative learning control with initial state learning for a class of fractional order two‐dimensional continuous‐discrete linear systems

Lan

Zheng

2022

Adaptive Control & Signal

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This article presents a PI-type iterative learning control (ILC) law with initial state learning for a class of 𝛼 (0 < 𝛼 ≤ 1) fractional order two-dimensional (2D) linear systems. First, by using backward difference method, the finite difference approximation of the fractional order derivative is obtained, which leads to globally 2 − 𝛼 order accuracy. Then, a PI-ILC law is constructed at the nodes and the convergence analysis of the iterative scheme is proved. A linear matrix inequality-based sufficient condition is derived to guarantee that the tracking error is asymptotically convergent. The obtained convergence condition is fractional order dependent. Most of the classical ILC conditions for fractional order one-dimensional linear systems fall into the special cases of this article. Finally, the simulation results show the effectiveness of the proposed control method.

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Section: Introductionmentioning

confidence: 99%

Finite difference based iterative learning control with initial state learning for a class of fractional order two‐dimensional continuous‐discrete linear systems

Lan

Zheng

2022

Adaptive Control & Signal

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“…Therefore, we introduce the idea of learning to improve the performance of MAS formation tracking. Some monographs 24 and papers 25‐31 have successfully introduced ILC into MAS to form control problems, but the ILC of noninstantaneous impulsive MAS is still blank.…”

Section: Introductionmentioning

confidence: 99%

Iterative learning control for multi‐agent systems with noninstantaneous impulsive consensus tracking

Qiu

Wang

2021

Intl J Robust & Nonlinear

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In this paper, we consider the consistent tracking problem of noninstantaneous impulsive multi‐agent systems. Take advantage of the repeatability of tracking tasks and the learning ability of each agent, we show that all agents of linear systems are driven to achieve a given asymptotical consensus as the number of iteration increases by using the standard D‐type learning law with the initial state learning rule. In addition, for nonlinear systems, we use PD‐type learning law with the initial state learning rule to prove that all agents are driven to achieve a given asymptotical consensus as the number of iteration increases. Finally, two numerical examples are given to verify the effectiveness of our algorithm.

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“…Iterative learning control belongs to a class of tracking control methods, which achieves the system to track the desired trajectory through multiple iterations control. For example, in the work of Luo et al (2020), a P-type and PI-type iterative learning control update law has been proposed for a nonlinear fractional order multi-agent system, which solves the problem of consensus tracking with fixed and iterative variable communication graphs. An improved iterative learning control method (Q-ILC) based on quadratic criterion has been developed in the work of Zhu et al (2020), which solves the trajectory tracking control problem of robot manipulators with uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Distributed adaptive event-triggered iterative learning controller based on a filtering network

Zhu

2021

Transactions of the Institute of Measurement and Control

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This paper develops a distributed adaptive event-triggered iterative learning controller over a filtering network for a class of tracking control systems. The proposed distributed control method is more robust than centralized control in terms of node failure and transmission constraints. In view of the limited network bandwidth, adaptive event-triggered mechanisms (AETMs) have been considered in the network communication process. The proposed AETM is characterized by introducing the dynamic threshold parameter, which provides benefits in data scheduling. The existence of filters and controllers is analyzed by Lyapunov stability theory and linear matrix inequality techniques, and their parameters are finally obtained. Moreover, some simulation results on a numerical example and a irrigation canal are presented to show the applicability of the obtained results.

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Iterative Learning Control for Locally Lipschitz Nonlinear Fractional-order Multi-agent Systems

Cited by 27 publications

References 34 publications

Finite difference based iterative learning control with initial state learning for a class of fractional order two‐dimensional continuous‐discrete linear systems

Finite difference based iterative learning control with initial state learning for a class of fractional order two‐dimensional continuous‐discrete linear systems

Iterative learning control for multi‐agent systems with noninstantaneous impulsive consensus tracking

Distributed adaptive event-triggered iterative learning controller based on a filtering network

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