Distributed Adaptive Dynamic Surface Containment Control for Uncertain Multiple Euler-Lagrange Systems

Chang, Chia-Wen; Chan, Wei-Shou; Wu, Chun-I

doi:10.1007/s12555-017-0076-4

Cited by 10 publications

(4 citation statements)

References 28 publications

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“…As result, the coordinated control has been widely used in various fields, for example, sensor networks, rescue, reconnaissance operations and so forth. [10][11][12] Initially, the coordinated control was mainly designed for coordination of multiple agents while with single leader mode, for example, the coordinated bipartite tracking control, 13 the fixed-time based coordinated control, 14,15 the event-triggered coordinated control, 16 the prescribed-performance-based coordinated control, 17 the leader-follower based coordinated control, 18 and just to numerate a few. Nevertheless, with the increasing demands for multiple complex tasks, the single leader based coordinated control may not be adequate for modern marine engineering.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, by virtue of the advantages of networked control techniques, 6–9 the coordinated control can enable multiple ASVs to work together to achieve a collective goal while with salient superiorities, including strong flexibility, high reliability, low cost and powerful regulation ability in completing various complex space tasks. As result, the coordinated control has been widely used in various fields, for example, sensor networks, rescue, reconnaissance operations and so forth 10–12 …”

Section: Introductionmentioning

confidence: 99%

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Containment control of networked heterogeneous autonomous surface vehicles: A data‐driven control approach

Weng,

Dai,

et al. 2024

Intl J Robust & Nonlinear

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In this article, by creating a novel distributed full‐form dynamic linearization based model‐free adaptive containment control (FFDL‐MFACC) approach, the containment control problem of networked heterogeneous autonomous surface vehicles (ASVs), suffering from complex external disturbances and unavailable kinetic model‐information, is resolved. In light of the data‐driven strategy, a full‐form dynamic linearization‐based data model is efficiently established. Then, by further deploying the commonly used rotation matrix, a distributed FFDL‐MFACC scheme is thereafter developed such that accurate tracking of a predefined convex hull spanned for all follower vehicles can be achieved. Afterwards, a disturbance observer is further designed to accurately estimate the lumped disturbances and the estimation is served as a compensation within the distributed full‐form dynamic linearization. Rigorously theoretical analysis indicates the devised distributed FFDL‐MFACC method can ensure the asymptotic containment tracking of networked heterogeneous ASVs. Finally, simulation results are illustrated to verify the advantages of the devised approach.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Containment control of networked heterogeneous autonomous surface vehicles: A data‐driven control approach

Weng,

Dai,

et al. 2024

Intl J Robust & Nonlinear

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“…Generally, when a system works under uncertain disturbances, we need to improve the robustness of the control as much as possible. Some scholars have studied the trajectory tracking method of EL systems; the common methods include the backstepping technique [8], dynamics surface control (DSC) [9], robust control [10,11], adaptive control [12,13], sliding mode control [14], and learning control [15]. Among them, the error restriction method can effectively enhance the robustness of the control.…”

Section: Introductionmentioning

confidence: 99%

Robust Tracking Control of the Euler–Lagrange System Based on Barrier Lyapunov Function and Self‐Structuring Neural Networks

Wang

2021

Computational Intelligence and Neuroscience

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This article studies the robust tracking control problems of Euler–Lagrange (EL) systems with uncertainties. To enhance the robustness of the control systems, an asymmetric tan-type barrier Lyapunov function (ATBLF) is used to dynamic constraint position tracking errors. To deal with the problems of the system uncertainties, the self-structuring neural network (SSNN) is developed to estimate the unknown dynamics model and avoid the calculation burden. The robust compensator is designed to estimate and compensate neural network (NN) approximation errors and unknown disturbances. In addition, a relative threshold event-triggered strategy is introduced, which greatly saves communication resources. Under the proposed robust control scheme, tracking behavior can be implemented with disturbance and unknown dynamics of the EL systems. All signals in the closed-loop system are proved to be bounded by stability analysis, and the tracking error can converge to the neighborhood near the origin. The numerical simulation results show the effectiveness and the validity of the proposed robust control scheme.

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“…The backstepping method, however, suffers from the problem of ‘explosion of complexity’. To overcome this drawback, dynamic surface control (DSC) method is introduced in [24, 25], where the derivatives of the virtual control signals are eliminated in the control signal, significantly reducing the computational burden of the distributed adaptive control scheme. In [26, 27], using DSC and a neural network (NN) approach, a consensus algorithm is developed for a MAS with unknown dynamics in a strict‐feedback form.…”

Section: Introductionmentioning

confidence: 99%

Distributed adaptive consensus tracking control for non‐linear multi‐agent systems with time‐varying delays

Zamani

Askari

Kamali

et al. 2020

IET Control Theory & Appl

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In this study, a novel distributed adaptive controller is provided for consensus control of high‐order non‐linear multi‐agent systems with unknown time‐varying delays. The system is subject to uncertain disturbances, and the agents' dynamics are not known. Unlike the existing literature, the proposed method does not require time‐delay terms in system dynamics to be bounded. A neural network is used to model the unknown non‐linear dynamics. Then, despite the destabilising effect of the unknown delays, some adaptive rules based on the dynamic surface control are designed to achieve the consensus objective. The semi‐global uniform boundedness of the resultant closed‐loop signals and the convergence of the tracking errors to a neighbourhood of the origin are shown mathematically. Simulations verify the effectiveness of the results.

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Distributed Adaptive Dynamic Surface Containment Control for Uncertain Multiple Euler-Lagrange Systems

Cited by 10 publications

References 28 publications

Containment control of networked heterogeneous autonomous surface vehicles: A data‐driven control approach

Containment control of networked heterogeneous autonomous surface vehicles: A data‐driven control approach

Robust Tracking Control of the Euler–Lagrange System Based on Barrier Lyapunov Function and Self‐Structuring Neural Networks

Distributed adaptive consensus tracking control for non‐linear multi‐agent systems with time‐varying delays

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