Approximately adaptive neural cooperative control for nonlinear multiagent systems with performance guarantee

Wang, Jing; Yang, Tianyu; Staskevich, Gennady; Abbe, Brian

doi:10.1080/00207721.2016.1186242

Cited by 12 publications

(3 citation statements)

References 28 publications

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“…Lemma 1 (see [19]). Assuming the functions g(t) ∈ L q [0, T] and h(t) ∈ L p [0, T], then the convolution generalized Yong inequality of the functions g(t) and h(t) is…”

Section: E Normmentioning

confidence: 99%

Fractional-Order Iterative Learning Control with Initial State Learning for a Class of Multiagent Systems

Pan

et al. 2020

Complexity

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To solve the consensus problem of fractional-order multiagent systems with nonzero initial states, both open- and closed-loop PDα-type fractional-order iterative learning control are presented. Considering the nonzero states, an initial state learning mechanism is designed. The finite time convergences of the proposed methods are discussed in detail and strictly proved by using Lebesgue-p norm theory and fractional-order calculus. The convergence conditions of the proposed algorithms are presented. Finally, some simulations are applied to verify the effectiveness of the proposed methods.

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“…Lemma 1 (see [19]). Assuming the functions g(t) ∈ L q [0, T] and h(t) ∈ L p [0, T], then the convolution generalized Yong inequality of the functions g(t) and h(t) is…”

Section: E Normmentioning

confidence: 99%

Fractional-Order Iterative Learning Control with Initial State Learning for a Class of Multiagent Systems

Pan

et al. 2020

Complexity

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“…Among the control methods, iterative learning control (ILC) can achieve full tracking in a limited time for the repetitive system [26][27][28]. erefore, the ILC algorithm is applied for IOMAS to solve the consensus problem recently, especially for complex MASs [29], MASs with switching topologies and communication time delays [30], and nonlinear MASs [31].…”

Section: Introductionmentioning

confidence: 99%

Consensus Tracking of Fractional‐Order Multiagent Systems via Fractional‐Order Iterative Learning Control

Pan

et al. 2019

Complexity

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In this work, the consensus problem of fractional-order multiagent systems with the general linear model of fixed topology is studied. Both distributed PDα-type and Dα-type fractional-order iterative learning control (FOILC) algorithms are proposed. Here, a virtual leader is introduced to generate the desired trajectory, fixed communication topology is considered, and only a subset of followers can access the desired trajectory. The convergence conditions are proved using graph theory, fractional calculus, and λ norm theory. The theoretical analysis shows that the output of each agent completely tracks the expected trajectory in a limited time as the iteration number increases for both PDα-type and Dα-type FOILC algorithms. Extensive numerical simulations are given to demonstrate the feasibility and effectiveness.

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“…However, for the existence of a solution to HJB equation, the method requires nonsingular square input matrices. A similar method is incorporated in [35] to reach an optimum state consensus controller for a team of nonlinear agents. Authors in [36] designed an optimal consensus based formation controller for a team of mobile robots via HJB equation by considering both full connectivity and partial connectivity for their assumed network.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Cooperative Control Design for State Consensus of Periodic Multiagent Systems

Ayatollahi

Majd

Nasrabadi

2018

Mathematical Problems in Engineering

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The problem of cooperative optimal control of multiagent systems with linear periodic continuous-time dynamics is considered. The state consensus problem is formulated as an optimal control problem in which the consensus requirement is reflected in the cost. The cost optimization of each subsystem is considered over finite horizon while the states of the agents converge to a common value, with a control signal that depends on the interactions of the neighboring subsystems. The proposed control law consists of a local and regional terms to capture local measurements and measurements due to interactions with the neighboring agents, respectively. These two terms are obtained by solving a Hamilton-Jacobi-Bellman partial differential equation. A numerical example is presented to demonstrate the effectiveness of the proposed method.

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Approximately adaptive neural cooperative control for nonlinear multiagent systems with performance guarantee

Cited by 12 publications

References 28 publications

Fractional-Order Iterative Learning Control with Initial State Learning for a Class of Multiagent Systems

Fractional-Order Iterative Learning Control with Initial State Learning for a Class of Multiagent Systems

Consensus Tracking of Fractional‐Order Multiagent Systems via Fractional‐Order Iterative Learning Control

An Optimal Cooperative Control Design for State Consensus of Periodic Multiagent Systems

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