Quantized Consensus Control for Second-Order Nonlinear Multi-agent Systems with Sliding Mode Iterative Learning Approach

Deng, Xiongfeng; Sun, Xiaodong; Liu, Ri

doi:10.1007/s42405-018-0043-1

Cited by 13 publications

(13 citation statements)

References 36 publications

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“…Accordingly, it can be gotten from (46) that (0) = −1 ( ) from (26) and̂(0) =̂− 1 ( ) from (27). Consequently, we get from (47)…”

Section: Proof Design the Following Lyapunov Function Candidatementioning

confidence: 93%

“…(4) and (7), and suppose that Assumptions 7 and 8 are held, and the communication topology G is connected. Let the distributed adaptive neural network iterative learning control protocol (24) and the adaptive updating laws (25), (26), and (27) be applied, then all the following agents can track the trajectory of leader; namely, lim →∞ ( ) = 0 ( ) and lim →∞ V ( ) = V 0 ( ) for = 1, ⋅ ⋅ ⋅ , over the interval [0, ].…”

Section: Convergence Analysismentioning

confidence: 99%

“…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: 96%

“…Currently, the method has been used to achieve the consensus problems of multiagent systems. 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.…”

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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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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“…Accordingly, it can be gotten from (46) that (0) = −1 ( ) from (26) and̂(0) =̂− 1 ( ) from (27). Consequently, we get from (47)…”

Section: Proof Design the Following Lyapunov Function Candidatementioning

confidence: 93%

Section: Convergence Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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show abstract

“…However, some progress has been made in quantized control only for single nonlinear uncertain systems [35]- [40] or single switched uncertain systems [41]. To the authors' knowledge, there have not been results to be reported on uncertain nonlinear MASs except [42], [43]. In [42], the quantized tracking control scheme is proposed based on sliding mode control approach for second-order nonlinear MASs, in which the nonlinearities are completely known.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Tracking Control for a Class of Uncertain Nonlinear Multi-Agent Systems With Input Quantization Based on Neural Approach

et al. 2019

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This paper mainly concentrates on the distributed tracking control problem for a class of uncertain nonlinear multi-agent systems with quantized input signal. Unlike the previous results on quantized control for multi-agent systems, the nonlinearities in this research can be completely unknown. To cope with these unknown nonlinearities, adaptive neural networks technique is employed to approximate the unknown nonlinear functions. Meanwhile, backstepping approach is utilized to handle the control design issue. Based on an important property of nonlinear decomposition for quantizer, a distributed quantized feedback tracking control scheme is successfully proposed to ensure the stability of the whole systems and the implementation of synchronous tracking. Finally, a simulation example is used to illustrate the efficacy of the proposed control scheme. INDEX TERMS Adaptive neural control, multi-agent systems, backstepping, quantized control.

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Dynamic boundary layer super‐twisting sliding mode control algorithm based on RBF neural networks for a class of leader‐follower multi‐agent systems

Jia,

Shangguan,

Zheng

2023

Intl J Robust & Nonlinear

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In this paper, the consensus problem of robust sliding mode fault tolerance for a class of leader‐follower multi‐agent systems is discussed. Aiming at a second‐order multi‐agent system with unknown model uncertainty and external disturbance, a new super‐twisting sliding mode control method based on a dynamic boundary layer and neural network is proposed. Firstly, the super‐twisting controller is designed by introducing two new variables, the convergence speed of the control system is greatly improved, and the symbolic function is replaced by an improved dynamic boundary layer, which will be continuously adjusted with the system states, the tracking accuracy of the system is improved, and the chattering problem caused by symbolic function is overcome effectively. Secondly, an radial basis function neural network is used to realize the adaptive approximation to the completely unknown model, so that the controller does not need to depend on the precise mathematical model of the controlled system, and the stability of the closed‐loop system is ensured by adjusting the adaptiveweight. Thirdly, the stability of the whole system is analyzed by the Lyapunov method, and the upper bound of robust consensus error is given by constructing an equivalence relation, meanwhile, the upper bound of the final convergence of the sliding variable in the dynamic boundary layer is analyzed. Finally, the simulation results for a second‐order system show the superior performance of the proposed control algorithm, then it is extended to a class of application systems, and the same conclusion is obtained.

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Quantized Consensus Control for Second-Order Nonlinear Multi-agent Systems with Sliding Mode Iterative Learning Approach

Cited by 13 publications

References 36 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

Adaptive Tracking Control for a Class of Uncertain Nonlinear Multi-Agent Systems With Input Quantization Based on Neural Approach

Dynamic boundary layer super‐twisting sliding mode control algorithm based on RBF neural networks for a class of leader‐follower multi‐agent systems

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