Robust model‐free adaptive iterative learning formation for unknown heterogeneous non‐linear multi‐agent systems

Ren, Yi; Hou, Zhongsheng

doi:10.1049/iet-cta.2019.0738

Cited by 50 publications

(42 citation statements)

References 43 publications

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“…The disturbance problem of discrete‐time nonlinear systems is discussed, and a mode‐free adaptive integral terminal sliding mode predictive control protocol is proposed in the work of Wang and Hou 40 . An ultra‐local model disturbance compensation method is proposed for a quadrotor UAV in the work of Al Younes et al 41 The robust MFAILC approaches to address measurement disturbance, and unknown disturbance problems are considered for MASs to perform consensus tracking tasks by Ren and Hou 42 and Wang et al, 43 respectively. To the best of our knowledge, existing data‐driven control approaches only consider the effect of output saturation or disturbance problems for a simple system 39,40,44 .…”

Section: Introductionmentioning

confidence: 99%

Model‐free adaptive consensus tracking control for unknown nonlinear multi‐agent systems with sensor saturation

Zhao

2021

Intl J Robust & Nonlinear

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This article proposes a distributed model‐free adaptive consensus tracking control (DMFACTC) approach for a class of unknown heterogeneous nonlinear discrete‐time multi‐agent systems (MASs) with sensor saturation and measurement disturbance to perform consensus tracking tasks. Meanwhile, both fixed and switching topologies are considered, where only a subset of agents can acquire the desired trajectory information in each topology. A time‐varying linear data model for each agent is first established by utilizing the dynamic linearization method to formulate this algorithm. Merely, the input data and the saturated output data with measurement disturbance of each agent are applied to construct the DMFACTC algorithm without employing any dynamics model information of MASs. The convergence of the designed scheme is strictly proved. It illustrates that the output saturation and switching topologies do not affect the stability of MASs. Moreover, even if the sensor saturation, measurement disturbance, and switching topologies happen simultaneously, the DMFACTC also guarantees that the tracking errors of MASs converge to a small range around the origin. Furthermore, two numerical simulations and a realistic filling system simulation further verify the correctness and effectiveness of the theoretical results.

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Section: Introductionmentioning

confidence: 99%

Model‐free adaptive consensus tracking control for unknown nonlinear multi‐agent systems with sensor saturation

Zhao

2021

Intl J Robust & Nonlinear

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“…Corollary 1 derives that the covariance matrix of tracking error e k converges to zero if ‖E[V k ]‖ converges to zero. It shows that the effects of both channel noise and data dropout on iterative learning controllers can be effectively eliminated by proposed scheme (16), and then, a perfect tracking can be guaranteed for MAS (3).…”

mentioning

confidence: 94%

“…And, the authors in [14] presented a novel ILC algorithm that did not rely on identical reference trajectories over the iteration domain. Besides, data-driven frameworks of ILC for MAS were proposed in [15,16]. Coincidentally, Bu et al made a more in-depth study on data-driven ILC and solved the problem of data quantization and limited sensor output [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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Average-ILC-Based Consensus Tracking of Multiagent Systems over Wireless Networks in Presence of Channel Noise and Data Dropout

Fang

Sheng

2021

Mathematical Problems in Engineering

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In a multiagent system (MAS), communication signals are affected by harsh wireless networks when they are transmitted from an agent to its neighboring agents, leading to the inconsistency of the MAS. In this paper, an average-iterative learning control (average-ILC) method is studied to address the consensus problem of MAS over wireless networks in the presence of channel noise and data dropout. The combined effects of channel noise and data dropout on iterative learning controllers are carefully analyzed. Based on graph theory and mathematical expectation, the corresponding average-iterative learning scheme is proposed. Especially, a sufficient condition is derived for the average-iterative learning scheme. Rigorous theoretical analysis demonstrates that the convergence of the covariance matrix of tracking error can be guaranteed with the help of an average-iterative learning scheme. Finally, simulation results are given to show the effectiveness of the proposed method.

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“…Yu et al (2019bYu et al ( , 2020 have considered the issue of time-varying formation for high-order nonlinear MASs. Model-free formation tracking for nonlinear MASs has been addressed by Ren and Hou (2020). However, these works depend on a matching condition for nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

Distributed adaptive time-varying formation control for Lipschitz nonlinear multi-agent systems

Yan

Zhang

Guo

et al. 2021

Transactions of the Institute of Measurement and Control

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In this paper, two kinds of distributed time-varying formation tracking problems for a class of Lipschitz nonlinear multi-agent systems are investigated, that is, the cases with leadless and leader-following formation. By devising a new type of edge-based adaptive algorithm, the controller of each agent can avoid the use of eigenvalues of Laplacian matrix in communication networks. The proposed algorithm only utilizes local information among neighbouring agents to form time-varying formation for nonlinear multi-agent systems with mismatched nonlinearities, and the formation tracking error is uniformly ultimately bounded. To accomplish the required time-varying formation characteristic, a predesigned formation compensation function is given. Finally, numerical simulation examples are presented to indicate the stability of the proposed algorithm, and a comparison is also given to show the superiority of the given algorithm.

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Robust model‐free adaptive iterative learning formation for unknown heterogeneous non‐linear multi‐agent systems

Cited by 50 publications

References 43 publications

Model‐free adaptive consensus tracking control for unknown nonlinear multi‐agent systems with sensor saturation

Model‐free adaptive consensus tracking control for unknown nonlinear multi‐agent systems with sensor saturation

Average-ILC-Based Consensus Tracking of Multiagent Systems over Wireless Networks in Presence of Channel Noise and Data Dropout

Distributed adaptive time-varying formation control for Lipschitz nonlinear multi-agent systems

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