Consensus control of leader-following nonlinear multi-agent systems with distributed adaptive iterative learning control

Deng, Xiongfeng; Sun, Xiaodong; Liu, Ri

doi:10.1080/00207721.2018.1535677

Cited by 15 publications

(10 citation statements)

References 38 publications

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“…Moreover, the iterative learning control for the high-order and heterogeneous multiagent systems were studied in [32,33], respectively. Different from [27][28][29][30][31][32][33], the iterative learning control method was applied to deal with the input saturation problem of robotic arm systems in [34], and the iterative learning control protocol for the nonrepetitive trajectory tracking of mobile robots with fault-tolerant and output constraints was presented in [35]. However, to the best of our knowledge, there are few papers that discussed the issue of the iterative learning control for the consensus problem of nonlinear multiagent systems with external disturbances and input saturation, which is the second motivation of this paper.…”

Section: Introductionmentioning

confidence: 97%

“…In [27], the consensus problem of multiagent systems with sliding mode iterative learning control was investigated. In [28,29], the tracking problems of multiagent systems were addressed by using the designed iterative learning control method. In [30], the formation tracking control problem with distributed formation iterative learning approach was discussed, while the nonrepetitive formation tracking problem of multiagent systems with line-of-sight and angle constraints under a novel iterative learning control method was discussed in [31].…”

Section: Introductionmentioning

confidence: 99%

“…(ii) Motivated by [28,36], the radial basis function (RBF) neural network is adopted to approximate the unknown nonlinear terms of all following agents in this paper. Also, it is supposed that the information of leader is not available to any following agents.…”

Section: Introductionmentioning

confidence: 99%

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Leader‐Following Consensus for Second‐Order Nonlinear Multiagent Systems with Input Saturation via Distributed Adaptive Neural Network Iterative Learning Control

2019

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In this paper, the consensus tracking control problem of leader-following nonlinear multiagent systems with iterative learning control is investigated. The model of each following agent consists of second-order unknown nonlinear dynamics and the external disturbance. Moreover, the input of each following agent is subject to saturation constraint. It is assumed that the information of leader is not available to any following agents, and the radial basis function neural network is introduced to approximate the nonlinear dynamics. Then, a distributed adaptive neural network iterative learning control protocol and the adaptive updating laws for the time-varying parameters are proposed, respectively. A new Lyapunov function is constructed to analyze the validity of the presented control protocol. Finally, a numerical example is provided to verify the effectiveness of theoretical results.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Leader‐Following Consensus for Second‐Order Nonlinear Multiagent Systems with Input Saturation via Distributed Adaptive Neural Network Iterative Learning Control

2019

Self Cite

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“…To relax the requirement of the strict IICs, [23] and [24] propose the ILC consensus protocol with the initial state learning laws. Moreover, the alignment condition is considered as another method alleviating the limitations of the strict ILCs, and some instructive results have been obtained for the consensus of IOMASs, such as first-order agents [25], second-order agents [26] and high-order agents [27]. The observer-type ILC protocols with the alignment action are designed for the consensus maintaining of IOMASs with the unmeasurable states [28], [29].…”

Section: Introductionmentioning

confidence: 99%

Iterative Learning Consensus of Fractional-Order Multi-Agent Systems Subject to Iteration-Varying Initial State Shifts

Wang

2019

IEEE Access

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This paper investigates the robust consensus tracking problem of fractional-order multiagent systems (FOMASs) subject to the iteration-varying initial state shifts. For the FOMASs including one leader agent and multiple follower agents, the PD α-type ILC protocol with the rectifying action is proposed. By improving the existing average operator and choosing the suitable variables, the leaderfollowing FOMASs under the proposed protocol are rewritten as a two-dimensional (2D) dynamical model. Based on the 2D analysis approach, the sufficient conditions are presented for the consensus of FOMASs. It is shown that due to the improved average operator, the derived sufficient conditions are more relaxed. With the increase of iteration step, the output of each follower agent will converge, and as the iteration step goes to infinity, and the limit output of each follower agent can be formulated in terms of the output of leader agent, the mean values of the initial output tracking errors, the learning gain matrices, the fractional order and the structure of communication graph. Finally, two numerical simulation examples are presented to demonstrate the effectiveness of the proposed method. INDEX TERMS Fractional-order multi-agent systems, iterative learning control, two-dimensional analysis approach, iteration-varying initial state shifts, consensus tracking.

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“…The composite energy function (CEF) method is one of the remedies for the bad transient phenomena, because the CEF method is defined in L 2 norm and can lead to point-wise convergence [28]. There have existed some instructive results of the CEF method in the perfect consensus problem for the linear IOMASs, including firstorder agents [29], second-order agents [30] and high-order agents [31]. The similar methods are applied to the perfect consensus problems of the nonlinear IOMASs with unknown control direction [32].…”

Section: Introductionmentioning

confidence: 99%

Performance Index Based Observer‐Type Iterative Learning Control for Consensus Tracking of Uncertain Nonlinear Fractional‐Order Multiagent Systems

Wang

Zhang

2019

Complexity

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This paper is devoted to the perfect tracking problem of consensus and the monotonic convergence problem of input errors for the uncertain nonlinear fractional-order multiagent systems (FOMASs), where there exist the linear coupling relations between the fractional order, the perturbations of the system matrix, and input matrix. For the FOMASs including one leader agent and multiple follower agents, an observer-type fractional-order iterative learning consensus protocol is proposed. Based on the two-dimensional analysis for the FOMASs, a novel performance index, which can exhibit the monotonic convergence of the input errors, is constructed by using the definition of fractional integral. A Lyapunov-like method is applied to derive the sufficient conditions in terms of linear matrix inequalities, which can guarantee the perfect tracking of consensus and the monotonic convergence of input errors. Finally, the numerical simulation results including comparisons to traditional two-dimensional analysis are presented to demonstrate the effectiveness of the proposed methods.

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Consensus control of leader-following nonlinear multi-agent systems with distributed adaptive iterative learning control

Cited by 15 publications

References 38 publications

Leader‐Following Consensus for Second‐Order Nonlinear Multiagent Systems with Input Saturation via Distributed Adaptive Neural Network Iterative Learning Control

Leader‐Following Consensus for Second‐Order Nonlinear Multiagent Systems with Input Saturation via Distributed Adaptive Neural Network Iterative Learning Control

Iterative Learning Consensus of Fractional-Order Multi-Agent Systems Subject to Iteration-Varying Initial State Shifts

Performance Index Based Observer‐Type Iterative Learning Control for Consensus Tracking of Uncertain Nonlinear Fractional‐Order Multiagent Systems

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