Data-Driven Robust Iterative Learning Consensus Tracking Control for MIMO Multiagent Systems Under Fixed and Iteration-Switching Topologies

Song, Weizhao; Zhang, Huaguang; Wang, Wei

doi:10.1109/tsmc.2020.3017289

Cited by 38 publications

(13 citation statements)

References 41 publications

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“…3.b shows that agents can maintain the desired tracking gap of the counter-trajectory. Figure 5 shows that the event-triggered times of each agent are recorded as 31,34,29,27,14,27,24 at l = 355. The average number of the event-triggered is about 26.57, so the designed ET-DMFAILBFC scheme can reduce about 73.43% energy costs of communication and computation for MASs with a fixed communication topology.…”

Section: Simulation a Fixed Topologymentioning

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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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: Simulation a Fixed Topologymentioning

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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“…A disturbance compensation method was studied by Li et al [28] and Ren et al [29] for MASs conducting consensus and formation tasks, respectively. Other interesting works can be found in [30], [31].…”

Section: Introductionmentioning

confidence: 99%

“…However, disturbance, especially measurement noise, is often encountered in practical systems, which reduces the control performance and even causes instability of controlled systems. For data-driven control, most of the results were focused on designing an estimator by using pseudo-partial-derivative (PPD) techniques such as [28], [29], [31], and [37]. Although using the PPD technique can reduce the bounded disturbances, the constraints are highly stringent, making the application performance of this method limited.…”

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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“…[1][2][3][4][5] In contrast with a single agent, MASs can complete some complex tasks in a more efficient manner since the agents in MASs can cooperate with each other to strengthen the capability in solving problems as well as the robustness. Among existing research works on MASs, the consensus control problem, as one of the most fundamental and significant hotspots, has attracted lots of attention and many techniques have been developed in this field such as neural-network-based control, [6][7][8] optimal control, [9][10][11] fuzzy control, 12,13 iterative learning control, [14][15][16] adaptive control [17][18][19][20] and so forth. Although there exist many works on consensus control, few papers quantitatively analyze the influences of MASs' inherent properties, which includes the model nonlinearity, the couplings within a single agent and among multiple agents, on convergence rate in the consensus process.…”

Section: Introductionmentioning

confidence: 99%

“…Practically, most real physical MASs possess properties of nonlinearity, time variability, 21 which could degrade the performance of MASs especially when coupled with external disturbances. 15,22 Backstepping techniques have the advantage in the controller design for nonlinear high-order systems because it can provide a systematic framework with the characteristics of clear logical recursion and easy implementation. Thus, this technique has been employed for the consensus control of MASs.…”

Section: Introductionmentioning

confidence: 99%

Robust consensus control of nonlinear multi‐agent systems based on convergence rate estimation

Xiong

et al. 2022

Intl J Robust & Nonlinear

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Convergence rate is an important performance criterion for consensus control of multi‐agent systems (MASs). It has been an important and difficult problem to accelerate the convergence rate of MASs. In this article, a robust consensus control scheme based on the convergence rate estimation for a nonlinear high‐order MAS is proposed to accelerate the convergence rate as well as improve the control performance of the MAS. In the proposed control scheme, a novel convergence rate indicator (CRI) is defined to measure the influence of the model nonlinearity, the state couplings within a single agent and among multiple agents in the MAS on convergence rate. Then, a distributed consensus controller with a CRI‐based additive term is designed by the backstepping technique with disturbance observers to enhance the robustness of the closed‐loop system. Furthermore, from a theoretical perspective, the robustness and the stability of the MAS are proved. Finally, the consensus control problem of the multi‐agent control system composed of five single‐link robot arms is simulated to validate the effectiveness of the proposed controller. Simulation results illustrate that the proposed consensus control approach with CRI has better performance than conventional backstepping consensus control techniques of the MAS in the same conditions.

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Data-Driven Robust Iterative Learning Consensus Tracking Control for MIMO Multiagent Systems Under Fixed and Iteration-Switching Topologies

Cited by 38 publications

References 41 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

Robust consensus control of nonlinear multi‐agent systems based on convergence rate estimation

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