Cooperative fuzzy adaptive output feedback control for synchronisation of nonlinear multi-agent systems under directed graphs

Wang, W.; Wang, D.; Peng, Zhouhua

doi:10.1080/00207721.2014.886135

Cited by 31 publications

(29 citation statements)

References 33 publications

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“…However, the unknown system dynamics functions were assumed to be in the range space of the control input. That is, the uncertainties must satisfy the matching condition in Peng et al (2014), Peng et al (2015), Wang et al (2015) and Shen et al (2015), which may limit their applications. To overcome those limitations, distributed adaptive leader-following consensus schemes were presented to achieve consensus with secondorder nonlinear strict-feedback dynamics under a directed connected graph in Chen et al (2015b).…”

Section: Introductionmentioning

confidence: 99%

“…However, the desired trajectory is assumed to be available to all agents in the formation. Peng et al (2014), Peng et al (2015), Wang et al (2015) and Shen et al (2015) considered the synchronisation problem of nonlinear multi-agent systems with unknown dynamics and a dynamic leader whose input is not available to any follower. Distributed neuro/fuzzy adaptive protocols were proposed to guarantee that the states of all followers synchronise to that of the leader.…”

Section: Introductionmentioning

confidence: 99%

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Distributed adaptive output feedback tracking control for a class of uncertain nonlinear multi-agent systems

Wang

Yan

et al. 2016

International Journal of Systems Science

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This paper addresses the distributed output feedback tracking control problem for multi-agent systems with higher order nonlinear non-strict-feedback dynamics and directed communication graphs. The existing works usually design a distributed consensus controller using all the states of each agent, which are often immeasurable, especially in nonlinear systems. In this paper, based only on the relative output between itself and its neighbours, a distributed adaptive consensus control law is proposed for each agent using the backstepping technique and approximation technique of Fourier series (FS) to solve the output feedback tracking control problem of multi-agent systems. The FS structure is taken not only for tracking the unknown nonlinear dynamics but also the unknown derivatives of virtual controllers in the controller design procedure, which can therefore prevent virtual controllers from containing uncertain terms. The projection algorithm is applied to ensure that the estimated parameters remain in some known bounded sets. Lyapunov stability analysis shows that the proposed control law can guarantee that the output of each agent synchronises to the leader with bounded residual errors and that all the signals in the closed-loop system are uniformly ultimately bounded. Simulation results have verified the performance and feasibility of the proposed distributed adaptive control strategy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed adaptive output feedback tracking control for a class of uncertain nonlinear multi-agent systems

Wang

Yan

et al. 2016

International Journal of Systems Science

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“…Peng, Wang, Sun, and Wang (2014) proposed a distributed NN adaptive control method using state feedback and then extending to output feedback for undirected connected communication topology. Wang, Wang, and Peng (2014) On the other hand, there are a few studies that discuss cooperation of multi-input/multi-output (MIMO) agents (Cheng et al, 2010;Hou et al, 2009;Peng et al, 2014;Wang et al, 2014;Zou & Kumar, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…AUVs and autonomous unmanned surface vehicles (ASVs) are examples of such systems which are in strict-feedback form (Li & Lee, 2005;Skjetne, Fossen, & Kokotovic, 2005). (2) In contrast to research works on unknown nonlinear MIMO systems which considered the gain matrix to be unity or constant (Cheng et al, 2010;Hou et al, 2009;Peng et al, 2014;Wang et al, 2014), in this work, the gain matrix of each agent is assumed to be the function of the states of the agent and to be unknown which complicates the problem solution.…”

Section: Introductionmentioning

confidence: 99%

Synchronisation of high-order MIMO nonlinear systems using distributed neuro-adaptive control

Sarand

Karimi

2014

International Journal of Systems Science

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This paper addresses synchronisation problem of high-order multi-input/multi-output (MIMO) multi-agent systems. Each agent has unknown nonlinear dynamics and is subject to uncertain external disturbances. The agents must follow a reference trajectory. An adaptive distributed controller based on relative information of neighbours of each agent is designed to solve the problem for any undirected connected communication topology. A radial basis function neural network is used to represent the controller's unknown structure. Lyapunov stability analysis is employed to guarantee stability of the overall system. By the theoretical analysis, the closed-loop control system is shown to be uniformly ultimately bounded. Finally, simulations are provided to show effectiveness of the proposed control method against uncertainty and disturbances.

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“…[8] and [9], respectively. Wang et al [10] reported the design of distributed state/output feedback cooperative control approaches for uncertain multi-agents in undirected communication graphs. This is later extended to a condition of directed graphs containing a spanning tree [11].…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Cooperative Control for Formation-Tracking Problem in a Network of Non-Affine Nonlinear Agents

Mahyuddin¹,

Safaei²

2017

Multi-Agent Systems

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In this chapter, a decentralized cooperative control protocol is proposed with application to any network of agents with non-affine nonlinear multi-input-multi-output (MIMO) dynamics. Here, the main purpose of cooperative control protocol is to track a time-variant reference trajectory while maintaining a desired formation. The reference trajectory is defined to a leader, which has at least one information connection with one of the agents in the network. The design procedure includes a robust adaptive law for estimating the unknown nonlinear terms of each agent's dynamics in a model-free format, that is, without the use of any regressors. Moreover, an observer is designed to have an approximation on the values of control parameters for the leader at the agents without connection to the leader. The entire design procedure is analysed successfully for the stability using Lyapunov stability theorem. Finally, the simulation results for the application of the proposed method on a network of nonholonomic wheeled mobile robots (WMR) are presented. Desirable leader-following tracking and geometric formation control performance have been successfully demonstrated through simulated group of wheeled mobile robots.

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Cooperative fuzzy adaptive output feedback control for synchronisation of nonlinear multi-agent systems under directed graphs

Cited by 31 publications

References 33 publications

Distributed adaptive output feedback tracking control for a class of uncertain nonlinear multi-agent systems

Distributed adaptive output feedback tracking control for a class of uncertain nonlinear multi-agent systems

Synchronisation of high-order MIMO nonlinear systems using distributed neuro-adaptive control

Robust Adaptive Cooperative Control for Formation-Tracking Problem in a Network of Non-Affine Nonlinear Agents

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