Distributed adaptive consensus tracking control of higher-order nonlinear strict-feedback multi-agent systems using neural networks

Wang, Gang; Wang, Chaoli; Li, Lin; Du, Qinghui

doi:10.1016/j.neucom.2016.06.013

Cited by 37 publications

(6 citation statements)

References 23 publications

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“…A robust method was introduced to solve this problem for consensus tracking in the works of Lv et al 30 and Li et al 31 Besides, the adaptive neural networks in the works of Wang et al 32,33 could also deal with the unknown control input. Although the 2 challenges were studied for the formation tracking problems in other works, [27][28][29][30][31][32][33] it was assumed that there was only 1 leader. In many practical applications, special task may need the followers to track the average state or other state forms of the multiple leaders, such as the cooperative working of the multiple manned/unmanned aerial vehicles.…”

Section: Discussionmentioning

confidence: 99%

“…[25][26][27] If h i (t) ≡ 0, the definition of the time-varying formation tracking become the one of the consensus tracking in other works. [30][31][32][33] In practical application, it is hard to achieve the asymptotic time-varying formation tracking proposed in other works 25,26,34,35 due to the uncertain nonlinearities/disturbances and the unknown control inputs of the leaders. If = 0, Definition 2 becomes the definition of the asymptotic time-varying formation tracking in other works.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…How to deal with the unknown control input of the leader becomes the second challenge. A robust method was introduced to solve this problem for consensus tracking in the works of Lv et al and Li et al Besides, the adaptive neural networks in the works of Wang et al() could also deal with the unknown control input. Although the 2 challenges were studied for the formation tracking problems in other works,() it was assumed that there was only 1 leader.…”

Section: Introductionmentioning

confidence: 99%

“…Time‐varying formation tracking problems for high‐order nonlinear multiagent systems were studied in the work of He et al; however, there was only 1 leader, and the control input was known to all the followers, which is restrictive. Formation tracking problems with unknown control input of the leader were studied in other works(); however, the formation between the followers were zero or time invariant. Although the time‐varying formation tracking problems with multiple leaders were investigated in the works of Dong et al,() the control inputs of the leaders were assumed to be zero, and the nonlinearity and the disturbance were not taken into account.…”

Section: Introductionmentioning

confidence: 99%

“…Third, the distributed disturbance observer is designed to estimate the uncertain nonlinearities, disturbances, and the unknown control inputs of multiple leaders. The robust method and the adaptive neural networks method were employed to process the nonlinearities/disturbances and the unknown control inputs in the works of Li et al,() and Wang et al,() respectively. However, the robust controller may cause chattering, and the neural networks may be a little complex and the real‐time performance may not be so good.…”

Section: Introductionmentioning

confidence: 99%

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Practical time‐varying formation tracking for high‐order nonlinear multiagent systems with multiple leaders based on the distributed disturbance observer

Dong

Liang

et al. 2018

Intl J Robust & Nonlinear

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Summary Practical time‐varying formation tracking analysis and design problems for high‐order nonlinear multiagent systems with directed interaction topologies are investigated by using the distributed disturbance observer, where the time‐varying formation tracking error can be controlled within an arbitrarily small bound. Different from the previous work, there exists a predefined time‐varying formation formed by the states of the followers and the formation tracks the convex combination of the states of the leaders with unknown control inputs. Besides, the leaders can be multiple, and the dynamics of each follower has heterogeneous nonlinearity and disturbance. First, a distributed disturbance observer‐based practical time‐varying formation tracking protocol is constructed using neighboring relative information, where only a part of the followers, which are named as well‐informed ones, are required to obtain the information of the multiple leaders. The proposed protocol can process the heterogeneous nonlinearity, the disturbance of each follower, and the unknown control inputs of the leaders simultaneously. Then, an algorithm with 2 steps is presented to design the practical time‐varying formation tracking protocol by solving an algebraic Riccati equation and an algebraic equation, where the time‐varying formation tracking feasibility condition is introduced. Moreover, the stability of the closed‐loop multiagent system under the proposed protocol is proved by using the properties of the Laplacian matrix and the Lyapunov stability theory. Finally, a numerical simulation example is provided to illustrate the effectiveness of the obtained theoretical results.

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Section: Discussionmentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Practical time‐varying formation tracking for high‐order nonlinear multiagent systems with multiple leaders based on the distributed disturbance observer

Dong

Liang

et al. 2018

Intl J Robust & Nonlinear

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Adaptive learning control for group consensus tracking of discrete nonlinear multiagent systems

Gao,

Li,

2024

Asian Journal of Control

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In this article, we explore the group output tracking consensus problem for discrete‐time strict‐feedback ‐order nonlinear multiagent systems that run repeatedly on finite time . A novel distributed adaptive iterative learning group consensus protocol is designed, which consists of two main components. The first component is based on time‐varying neural networks, which is used to approximate the unknown nonlinear function in the ‐step ahead predictor. In general, not all followers can access the information regarding the leader, which complicates the design of iterative learning protocols for MASs. Therefore, the second component of the protocol addresses this challenge by treating the leader's output as a time‐varying parameter and designing a time‐varying auxiliary term to compensate the leader's output information. Parameter updating laws and initial state learning laws are also proposed via the cooperative‐competitive relationship between the agents. We demonstrate the group consensus with sufficient small errors can be achieved at time , as the number of iterations proceed to infinity. Then, the results are extended to the case of multisubgroups and multileaders. Finally, two simulations validate the findings of this article.

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Fault‐tolerant consensus for switched multiagent systems with input saturation

Yin

Wang

Liu

et al. 2021

Intl J Robust & Nonlinear

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This article investigates the fault-tolerant consensus problem for switched multiagent systems with input saturation. Actuator faults including partial loss of effectiveness, outage, bias, and stuck are all considered in switched general linear systems. By utilizing the adaptive projection algorithm, low gain feedback theory, and average dwell time method, novel saturated consensus protocols are designed for both fixed and switching topologies with local information of neighboring agents. It is proven that the fault-tolerant consensus problem can be solved with the proposed protocols in both leader-following case and leaderless case. The innovation of this article lies not only in the fact that actuator faults, switched dynamics, and input saturation are simultaneously considered, but also in the fact that a novel modified low-and-high gain feedback approach is introduced to deal with various types of actuator faults. Finally, two numerical examples are given to validate the effectiveness of the theoretical results.

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Distributed adaptive consensus tracking control of higher-order nonlinear strict-feedback multi-agent systems using neural networks

Cited by 37 publications

References 23 publications

Practical time‐varying formation tracking for high‐order nonlinear multiagent systems with multiple leaders based on the distributed disturbance observer

Practical time‐varying formation tracking for high‐order nonlinear multiagent systems with multiple leaders based on the distributed disturbance observer

Adaptive learning control for group consensus tracking of discrete nonlinear multiagent systems

Fault‐tolerant consensus for switched multiagent systems with input saturation

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