Quantized model-free adaptive iterative learning bipartite consensus tracking for unknown nonlinear multi-agent systems

Zhao, Huarong; Yu, Hongnian

doi:10.1016/j.amc.2021.126582

Cited by 16 publications

(12 citation statements)

References 38 publications

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“…where c is often set as 10 −3 or 10 −4 . Remark 3: The existing MFAILC approaches [23]- [25] require information among agents to be transmitted in realtime, which causes energy costs and computational burden. It is noted that the designed event-triggered ILC method can relieve the above issues.…”

Section: A Event-triggered Estimation Of Ppdmentioning

confidence: 99%

“…To address the above modeling problems, several datadriven control (DDC) schemes have been developed, for example, model-free adaptive control (MFAC) [20]- [22], model-free adaptive iterative learning control (MFAILC) [23]- [25], virtual reference feedback tuning (VRFT) [26], [27], and reinforcement learning (RL) [28]- [30]. The DDC approaches' main characteristic is to design a controller for the controlled plant without requiring knowledge of the controlled plant structure, only utilizing the controlled system's input/output (I/O) measurement data.…”

Section: Introductionmentioning

confidence: 99%

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Event-Triggered Distributed Data-Driven Iterative Learning Bipartite Formation Control for Unknown Nonlinear Multiagent Systems

Zhao

2024

IEEE Trans. Neural Netw. Learning Syst.

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In this study, we investigate the event-triggering time-varying trajectory bipartite formation tracking problem for a class of unknown nonaffine nonlinear discrete-time multiagent systems (MASs). We first obtain an equivalent linear data model with a dynamic parameter of each agent by employing the pseudo partial derivative technique. Then we propose an event-triggered distributed model-free adaptive iterative learning bipartite formation control scheme by using the input/output data of MASs without employing either the plant structure or any knowledge of the dynamics. To improve the flexibility and network communication resource utilization, we construct an observer-based event-triggering mechanism with a dead-zone operator. Furthermore, we rigorously prove the convergence of the proposed algorithm, where each agent's time-varying trajectory bipartite formation tracking error is reduced to a small range around zero. Finally, four simulation studies further validate the designed control approach's effectiveness, demonstrating that the proposed scheme is also suitable for the homogeneous MASs to achieve time-varying trajectory bipartite formation tracking.

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Section: A Event-triggered Estimation Of Ppdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Event-Triggered Distributed Data-Driven Iterative Learning Bipartite Formation Control for Unknown Nonlinear Multiagent Systems

Zhao

2024

IEEE Trans. Neural Netw. Learning Syst.

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“…Proof: Case 1: ∆y r (k) = 0. According to Equation (19), Equations ( 13) and ( 16) can be rewritten as…”

Section: Extension To Switching Topologiesmentioning

confidence: 99%

“…However, using first principles or identification for modeling complex nonlinear MASs is extremely difficult or impossible to obtain accurate dynamics [13]. To bypass the effects of inaccurate dynamics, an alternative method was developed, namely data-driven control, for instants, reinforcement learning [14], [15], Q-learning [16], data-driven iterative learning [17]- [19], adaptive dynamic programming [20], model-free adaptive control (MFAC) [21]- [24]. MFAC is a class of datadriven control for discrete-time nonlinear systems without establishing a neural network, which was first investigated by Hou et al [25].…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Robust Bipartite Consensus Control for a Class of Multiagent Systems

Zhao

Shan

2023

IEEE Trans. Ind. Electron.

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This paper studies the robust bipartite consensus problems for heterogeneous nonlinear nonaffine discrete-time multiagent systems (MASs) with fixed and switching topologies against data dropout and unknown disturbances. At first, the controlled system's virtual linear data model is developed by employing the pseudo partial derivative technique, and a distributed combined measurement error function is established utilizing a signed graph theory. Then, an input gain compensation scheme is formulated to mitigate the effects of data dropout in both feedback and forward channels. Moreover, a data-driven learning-based robust bipartite consensus control (LRBCC) scheme based on a radial basis function neural network observer is proposed to estimate the unknown disturbance, using the online input/output data without requiring any information on the mathematical dynamics. The stability analysis of the proposed LRBCC approach is given. Simulation and hardware testing also illustrate the correctness and effectiveness of the designed method.

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“…Lemma 1 [36]: Each row of the weighted Laplacian matrix sums to zero and contains at least one eigenvalue of 0. Concerning matrix L , several statements are equivalent as follows:…”

Section: Basic Algebraic Graph Theorymentioning

confidence: 99%

Bipartite Tracking Formation Control of Nonlinear Multi-Agent Systems Using Adaptive Output–Feedback Protocols

Liu

et al. 2022

IEEE Access

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This paper proposed distributed adaptive formation control for leader-follower bipartite timevarying formation (BTVF) of a nonlinear multi-agent system (MAS). The proposed nonlinear MAS satisfies the one-sided Lipschitz-type condition. In the topological graphs with directed spanning trees, the design of adaptive protocols does not depend on the known communication topology, which can avoid the use of global information. Given limited information, the proposed MAS can achieve desired formation tracking by utilizing an observer protocol and converting the bipartite formation control problem into a stability problem of system errors. The analysis results of the systematic errors by using Lyapunov candidate functions, indicate that the MAS can be globally stable with a certain convergence rate during operation. Finally, numerical simulations are presented to confirm the validity of the proposed approach.

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Quantized model-free adaptive iterative learning bipartite consensus tracking for unknown nonlinear multi-agent systems

Cited by 16 publications

References 38 publications

Event-Triggered Distributed Data-Driven Iterative Learning Bipartite Formation Control for Unknown Nonlinear Multiagent Systems

Event-Triggered Distributed Data-Driven Iterative Learning Bipartite Formation Control for Unknown Nonlinear Multiagent Systems

Learning-Based Robust Bipartite Consensus Control for a Class of Multiagent Systems

Bipartite Tracking Formation Control of Nonlinear Multi-Agent Systems Using Adaptive Output–Feedback Protocols

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