Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

Shen, Dong; Zhang, Chao; Xu, Jian‐Xin

doi:10.1002/rnc.4627

Cited by 27 publications

(15 citation statements)

References 46 publications

(113 reference statements)

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“…Under the assumption of boundedness of the nonlinear uncertainties dynamics, authors in [55] propose a distributed adaptive observer-based consensus protocol along with an adaptive mechanism for updating the coupling weight values. Leveraging, instead, neural networks tools, a distributed learning consensus protocol is suggested in [27,41,53,59] for solving a leader tracking problem with undirected communication graph topologies. Moreover, by combining a linear and discontinuous feedback terms with neural network approximation, [62] proposes a robust adaptive distributed controller for solving consensus of uncertain MAS.…”

Section: Introductionmentioning

confidence: 99%

Leader tracking control for heterogeneous uncertain nonlinear multi-agent systems via a distributed robust adaptive PID strategy

2022

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This paper addresses the leader tracking control problem for heterogeneous uncertain nonlinear multi-agent systems sharing information via a communication network, modeled as a directed graph. To solve the problem, we propose a novel distributed PID-like control strategy which, enhanced with a Lyapunov-based adaption mechanism for the control parameters, is able to counteract the uncertainties acting on the agents dynamics. The stability analysis analytically proves the effectiveness of the proposed PID protocol in ensuring the leader-tracking as well as the boundedness of the adaptive control gains. Numerical simulations, involving both the synchronization control problem for nonlinear harmonic oscillators and the practical engineering problem of the cooperative driving for autonomous connected vehicles, confirm the theoretical derivation and disclose the capability of the proposed strategy in achieving the control objective.

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Section: Introductionmentioning

confidence: 99%

Leader tracking control for heterogeneous uncertain nonlinear multi-agent systems via a distributed robust adaptive PID strategy

2022

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“…In Reference 26, a fractional‐order derivative with a memory property was introduced for linear and nonlinear ODMAS and the

P{D}^{\alpha }

‐type and

{D}^{\alpha }

‐type ILC protocols were designed separately to achieve the consensus control. More results on ODMAS can refer to References 4,5,27‐29. Compared with ODMAS, the literature on DPMAS is rather limited.…”

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

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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.

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“…Recent results in this regard are reported for biotechnological processes [46], multi-agent systems [47] and transportation systems [48]. Neural networks can be used instead of fuzzy models in this second approach as suggestively illustrated in [49] and [50].…”

Section: Introductionmentioning

confidence: 99%

Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

Precup¹,

Roman²,

Hedrea³

et al. 2022

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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This paper is dedicated to the memory of Prof. Ioan Dzitac, one of the fathers of this journal and its founding Editor-in-Chief till 2021. The paper addresses the performance improvement of three Single Input-Single Output (SISO) fuzzy control systems that control separately the positions of interest of tower crane systems, namely the cart position, the arm angular position and the payload position. Three separate low-cost SISO fuzzy controllers are employed in terms of first order discrete-time intelligent Proportional-Integral (PI) controllers with Takagi-Sugeno-Kang Proportional-Derivative (PD) fuzzy terms. Iterative Learning Control (ILC) system structures with PD learning functions are involved in the current iteration SISO ILC structures. Optimization problems are defined in order to tune the parameters of the learning functions. The objective functions are defined as the sums of squared control errors, and they are solved in the iteration domain using the recent metaheuristic Slime Mould Algorithm (SMA). The experimental results prove the performance improvement of the SISO control systems after ten iterations of SMA.

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Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

Cited by 27 publications

References 46 publications

Leader tracking control for heterogeneous uncertain nonlinear multi-agent systems via a distributed robust adaptive PID strategy

Leader tracking control for heterogeneous uncertain nonlinear multi-agent systems via a distributed robust adaptive PID strategy

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

Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

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