Distributed Leaderless and Leader-Following Consensus Control of Multiple Euler-Lagrange Systems with Unknown Control Directions

Wang, Gang; Wang, Chaoli; Cai, Xuan; Ji, Yunfeng

doi:10.1007/s10846-017-0554-1

Cited by 21 publications

(21 citation statements)

References 41 publications

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“…As pointed out in [31], the control directions of all ELSs are assumed to be the same is reasonable. For example, the consensus can only be achieved for those ELSs that have the same control directions.…”

Section: Remarkmentioning

confidence: 99%

“…Chen et al [29,30] considered the Nussbaum function-based control algorithms for the manipulator system under the affection of UCDs, but only the single ELS is considered. Although [31] studied the consensus problem for multiple ELSs under UCDs, the traditional backstepping design was used there.…”

Section: Introductionmentioning

confidence: 99%

“…(1) Compared with the traditional backstepping and DSC-based controller design methods in [14,18,31], the proposed command-filtered backstepping-based controller design method can not only avoid the traditional explosion terms but also eliminate the error caused by the used command filter by using the compensation system. (2) Compared with the command-filtered backsteppingbased controller design method in [22] with the complete known system model, the proposed adaptive neural control design method can solve the problem of unknown nonlinear dynamics by using NN approximation technique.…”

Section: Introductionmentioning

confidence: 99%

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

Meng

Zhao

2020

Complexity

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This paper investigates the distributed adaptive neural consensus tracking control for multiple Euler-Lagrange systems with parameter uncertainties and unknown control directions. Motivated by the Nussbaum-type function and command-filtered backstepping technique, the error compensations and neural network approximation-based adaptive laws are established, which can not only overcome the computation complexity problem of backstepping but also make the consensus tracking errors reach to the desired region although the control directions and system nonlinear dynamics are both unknown. Numerical example is given to show the proposed algorithm is effective at last.

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Section: Remarkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Meng

Zhao

2020

Complexity

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“…In order to facilitate the design of the controller, system (16) can be regarded as a cascade system, which is composed of a first-order subsystem and a second-order subsystem. Firstly, robust consensus control protocol is designed for these two subsystems, and then the final consensus control protocol is designed for the system (16) based on switch strategy.…”

Section: Controller Design Under Input Disturbancementioning

confidence: 99%

“…Similar research can refer to the literature. [14][15][16][17] In the leaderless structure system, although lots of consensus protocols can be designed to the states of system achieve agreement, the expected common values of state convergence are often uncontrollable. The consensus algorithms designed with leader-follower structure can solve these problems efficiently.…”

Section: Introductionmentioning

confidence: 99%

Consensus in networked multi-robot systems via local state feedback robust control

Zhang

Cao

2019

International Journal of Advanced Robotic Systems

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In the article, the consensus problem is considered for networked multi-robot systems, in which the dynamical equation of all robots is non-holonomic and nonlinear systems. In the multi-robot systems, each robot updates its current states and receives the states from the neighboring robots. Under the assumption that if the network graph is bidirectional, a local information-based state feedback robust controller is designed to make sure the convergence of the individual robots’ states to a common value. Finally, the effectiveness of the presented method is illustrated by the simulation results of a group of four mobile robots.

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Adaptive fuzzy finite‐time consensus tracking for multiple Euler‐Lagrange systems with unknown control directions

LiuGuoqing

Zhaolin

2020

Adaptive Control & Signal

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In this paper, the problem of adaptive fuzzy finite-time consensus tracking control for multiple Euler-Lagrange systems (ELSs) with uncertain dynamics and unknown control directions (UCDs) is investigated. The computational complexity problem in conventional backstepping is avoided by using finite-time command filter (FTCF), and the error in the filtering process is eliminated through error compensation signals. The fuzzy logic system combined with the adaptive control technique is applied to approximate and estimate the unknown nonlinear dynamics of ELS. The Nussbaum function-based continuous and nonsmooth input control torque is established to eliminate the influence of UCDs, and the proposed control scheme can guarantee the consensus tracking errors converge to the desired neighborhood of the origin within a finite time. Numerical simulation is used to test the effectiveness of the given algorithm.

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Distributed Leaderless and Leader-Following Consensus Control of Multiple Euler-Lagrange Systems with Unknown Control Directions

Cited by 21 publications

References 41 publications

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

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

Consensus in networked multi-robot systems via local state feedback robust control

Adaptive fuzzy finite‐time consensus tracking for multiple Euler‐Lagrange systems with unknown control directions

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