Distributed Adaptive Neural Consensus Tracking Control for Multiple Euler-Lagrange Systems with Unknown Control Directions

Meng, Fanfeng; Zhao, Lin; Yu, Jinpeng

doi:10.1155/2020/6061852

Cited by 2 publications

(4 citation statements)

References 34 publications

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“…Remark 2.1. The above four assumptions are conventional for consensus control of multiple Euler-Lagrange systems with disturbances or actuator faults (see [14,18,22,28,9,2,19,4,33,3,21,32]). In fact, Assumption 2.1 implies that the leader is a root node of the graph G, and hence H is invertible (see [7]), which is necessary for control design.…”

Section: Assumption 22mentioning

confidence: 99%

“…Over the past decades, the consensus problem of a multi-agent system composed of multiple EL equations has attracted much attention due to its widespread applications in practical engineering. Noting that uncertainties inevitably exist in the description of the dynamics of specified mechanism systems which give rise to essential obstacles in control design, several control schemes have been proposed on this topic based on the compensation of system uncertainties, such as robust control [10,6], adaptive control [1,25], sliding mode control [31], fuzzy control [14],and neural network control [18,23,22]. With the wide use of network communication in practical engineering, the saving of communication resources has become more and more important.…”

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confidence: 99%

“…With the wide use of network communication in practical engineering, the saving of communication resources has become more and more important. However, all the aforementioned literature [10,6,1,25,31,14,18,23,22] is based on time-triggered sampling of control signals, which is limited in the saving of network resources.…”

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confidence: 99%

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Event-triggered consensus control for a class of uncertain multiple Euler-Lagrange systems with actuator faults

Li,

Zhao,

Liang

et al. 2024

DCDS-S

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This paper is devoted to the event-triggered consensus control for a class of uncertain multiple Euler-Lagrange (EL) systems with actuator faults. Different from the related works where strict conditions are imposed on system uncertainties and the measurements of the leader's output, more serious uncertainties are involved since all the system dynamic matrices are unknown while both actuator faults and external disturbance are considered; and moreover, fewer measurements of the leader's output are required since its time derivatives are not necessarily available for feedback. Mainly because of these, the consensus problem is hard to solve by straightforwardly extending the existing results. To solve the control problem, a dynamic gain with a smart choice of its updating law is introduced to overcome the serious uncertainties and the sampling error of the control signal. By incorporating the dynamic gain into the vectorial backstepping procedure, an adaptive consensus controller joined with an event-triggered mechanism is designed for each follower to ensure the consensus of the multi-agent system in the sense that all the states of the closed-loop system are bounded while the output of each follower tracks the leader's output. Finally, the effectiveness of the proposed method is verified by a simulation example.

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Section: Assumption 22mentioning

confidence: 99%

mentioning

confidence: 99%

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Event-triggered consensus control for a class of uncertain multiple Euler-Lagrange systems with actuator faults

Li,

Zhao,

Liang

et al. 2024

DCDS-S

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“…In [11], distributed consensus control is investigated when the leader and followers both are in strict-feedback form with unknown parameters. In [12], the authors focus on adaptive neural consensus control for Euler-Lagrange systems with unknown control direction and parametric uncertainties. In [13], a dynamic event-triggered control strategy is adopted for consensus in time-delayed uncertain strict-feedback MAS.…”

Section: Introductionmentioning

confidence: 99%

Observer‐based adaptive emotional command‐filtered backstepping for cooperative control of input‐saturated uncertain strict‐feedback multi‐agent systems

Parsa

Akbarzadeh-T

Baghbani

2023

IET Control Theory & Appl

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This paper introduces a distributed observer-based emotional command-filtered backstepping (DOECFB) approach for leader-following cooperative output-feedback control of heterogenous strict-feedback multi-agent systems (MAS) under mismatched uncertainties and input saturation. A novel state observer is designed based on radial-basis emotional neural networks (RBENNs) that approximate uncertainties of model dynamics. To model inter-agent dynamics with less complexity, emotion-inspired approximated dynamics are shared among neighbouring followers, like emotional contagion in a group of people. An auxiliary system is also used to attenuate input saturation's negative effect on the cooperative tracking performance. Also, command filters and compensating signals are applied to avoid the 'explosion of complexity' in the backstepping design. Only local information from other agents is required for the proposed approach to guarantee convergence of the cooperative tracking error to a small region around zero and cooperatively semiglobally uniformly ultimately boundedness of closed-loop signals. Simulation examples on a second-order uncertain MAS and multiple forced-damped pendulums are conducted, and quantitative comparisons verify the effectiveness of DOECFB and the proposed observer.

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Distributed Adaptive Neural Consensus Tracking Control for Multiple Euler-Lagrange Systems with Unknown Control Directions

Cited by 2 publications

References 34 publications

Event-triggered consensus control for a class of uncertain multiple Euler-Lagrange systems with actuator faults

Event-triggered consensus control for a class of uncertain multiple Euler-Lagrange systems with actuator faults

Observer‐based adaptive emotional command‐filtered backstepping for cooperative control of input‐saturated uncertain strict‐feedback multi‐agent systems

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