PI-Type Iterative Learning Consensus Control for Second-Order Hyperbolic Distributed Parameter Models Multi-Agent Systems

Li, Peng; Liu, Xiao-Qing; Lan, Yong‐Hong

doi:10.1109/access.2020.2963991

Cited by 6 publications

(3 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By introducing radial basis function neural networks and proposing distributed adaptive ILC protocols, 30 guaranteed the consensus of the given DPMAS. Using nearest neighbor knowledge, 31 proposed

PI

‐type ILC protocols to study the consensus control problem of second‐order hyperbolic DPMAS. By introducing the high‐order internal model based

P

‐type ILC protocols for first‐order hyperbolic DPMAS, 32 realized the perfect trajectory tracking on

{L}^2

space.…”

Section: Introductionmentioning

confidence: 99%

Iterative learning based consensus control for distributed parameter type multi‐agent differential inclusion systems

Zhou

Wang

Shen

2022

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This article considers the learning consensus problem of distributed parameter type multi‐agent differential inclusion systems including parabolic type and hyperbolic type. By imposing a Lipschitz condition on a set‐valued mapping and utilizing distributed P$$ P $$‐type iterative learning consensus control protocols, an iterative learning process is established. Further, sufficient conditions for the convergence of the consensus error between any two agents in the sense of the certain norm are obtained with the help of Steiner‐type selectors. Finally, numerical simulations verify the validity of the proposed method.

show abstract

PI

‐type ILC protocols to study the consensus control problem of second‐order hyperbolic DPMAS. By introducing the high‐order internal model based

P

‐type ILC protocols for first‐order hyperbolic DPMAS, 32 realized the perfect trajectory tracking on

{L}^2

space.…”

Section: Introductionmentioning

confidence: 99%

Iterative learning based consensus control for distributed parameter type multi‐agent differential inclusion systems

Zhou

Wang

Shen

2022

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…In a similar vein, working on the finite dimensional decomposition of infinite dimensional dissipative systems, an

H_{\infty}

design was presented in Reference 12. Combining consensus controllers with iterative learning for second‐order distributed parameter systems was considered in Reference 13. Introducing the concept of a leader‐follower tracking in networked systems, each governed by a diffusion PDE was considered in Reference 14 and which utilized boundary sensing in each agent.…”

Section: Introductionmentioning

confidence: 99%

Modal consensus observers for distributed parameter systems

Demetriou

2021

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This article proposes a new type of a consensus protocol for the synchronization of distributed observers in systems governed by parabolic partial differential equations. Addressing the goal of sharing useful information among distributed observers, it delves into the details governing the modal decompositions of distributed parameter systems. Assuming that two different groups of sensors are available to provide process information to the two distributed observers, the proposed modal consensus design ensures that only useful information is transmitted to the requisite modal components of each of the observers. Without any consensus protocol, the observers capture different frequency content of the spatial process in differing degrees, as it relates to the concept of modal observability. Their modal components exhibit different learning behavior toward the process state. In the extreme case, it turns out that certain modal components of the distributed observers occasionally behave as naïve observers. To ensure that, both collectively and modal componentwise, the observers agree both with the process state and with each other, a modal component consensus protocol is proposed. Such a consensus protocol is mono‐directional and provides only useful information necessary to the appropriate modal component of the distributed filters that behaves as a naïve modal observer. This protocol, when abstracted and applied to different state decompositions can be viewed as mono‐directional projections of information transmitted and received by the participating distributed observers. Detailed numerical studies of advection PDE in one and two spatial dimensions are included to elucidate the details of the proposed modal consensus observers.

show abstract

“…Iterative learning control (ILC) has been widely utilized to cope with the repeated tracking control with high precision requirement in the fixed time interval due to its simplicity and effectiveness [8], [9]. Hence, ILC has been successfully implemented to many kinds of multi-agent systems in recent references, such as high-order nonlinear MASs [10], singular MASs [11], fractional-order MASs [12], and distributed parameter MASs [13]- [15], etc.. In [16], [17], the formation control problems of nonlinear MASs under switching interaction topologies were addressed by employing the ILC scheme.…”

Section: Introductionmentioning

confidence: 99%

Consensus Tracking Via Iterative Learning Control for Singular Fractional-Order Multi-Agent Systems Under Iteration-Varying Topologies and Initial State Errors

Wang

Zhou

et al. 2020

IEEE Access

View full text Add to dashboard Cite

This paper investigates the leader-following consensus tracking problems via iterative learning control for singular fraction-order multi-agent systems in the presence of iteration-varying switching topologies and initial state errors. First, in order to eliminate the impulsive effect of singular systems and handle iteration-varying topologies, the closed-loop D α-type iterative learning control protocol is proposed. To deal with initial state errors, the initial state learning laws are introduced in light of the initial output errors of each follower agent. The developed D α-type learning protocols based on initial state learning laws can guarantee each follower track perfectly the leader agent in the fixed time interval. Next, the sufficient convergent conditions of consensus tracking errors are provided. Moreover, the D α-type learning protocols are extended to nonlinear singular fraction-order multi-agent systems with iteration-varying topologies and initial state errors. Finally, two numerical examples are presented to verify the validity of the proposed D α-type learning scheme in this paper.

show abstract

PI-Type Iterative Learning Consensus Control for Second-Order Hyperbolic Distributed Parameter Models Multi-Agent Systems

Cited by 6 publications

References 35 publications

Iterative learning based consensus control for distributed parameter type multi‐agent differential inclusion systems

Iterative learning based consensus control for distributed parameter type multi‐agent differential inclusion systems

Modal consensus observers for distributed parameter systems

Consensus Tracking Via Iterative Learning Control for Singular Fractional-Order Multi-Agent Systems Under Iteration-Varying Topologies and Initial State Errors

Contact Info

Product

Resources

About