Distributed Consensus Backstepping Control in Networked Flexible Joint Manipulator Systems

Ying, Chengye; Liu, Lixia; Miao, Zhonghua; Zhou, Jin

doi:10.1007/978-981-15-8458-9_16

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…Reference 12 investigated an efficient and different compliance modeling theory for cooperative elastic joint robots. Reference 13 offered a decentralized controller to gain a solution for the leaderless consensus issues with undirected network topology.…”

Section: Introductionmentioning

confidence: 99%

Cooperative multiple elastic‐joint arms control using the (p, q)‐analogue of Bernstein operators as the uncertainty approximator

Bayat

Izadbakhsh

2023

Intl J Robust & Nonlinear

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This paper presents the position-force tracking control problem of cooperative multiple elastic-joint robotic arms, encountering model nonlinearities, unknown perturbations, and dynamic uncertainties. To this mean, an adaptive function approximation technique (FAT) is proposed, authorizing the reference trajectory to be tracked by the object. This potential originates from the universal approximation feature of the FAT-based methodologies. The (p, q)-analogue of the Bernstein operators is utilized for this purpose. Since the systems' parameters are not precisely known, adaptive laws are employed for adjusting the uncertainty coefficients. The Lyapunov stability theorem assures that all signals in the error space remain uniformly ultimately bounded (UUB). In the end, two elastic-joint arms carrying a rigid object are utilized for validation of the theoretical outcomes. The suggested strategy is also compared to the Chebyshev neural network (CNN). The results show the benefit of the existing method, managing the system even in the presence of disturbances and uncertainties with lower tuning parameters compared with CNN.

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Section: Introductionmentioning

confidence: 99%

Cooperative multiple elastic‐joint arms control using the (p, q)‐analogue of Bernstein operators as the uncertainty approximator

Bayat

Izadbakhsh

2023

Intl J Robust & Nonlinear

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“…(Xu and He, 2022) proposed a different and efficient compliance modeling theory for collaborative flexible-joint robots, on the basis of mechanical and electrical passive network synthesis to offer the systematic and theoretical guidance for the compliance design of the rotary flexible joint. (Zheng et al, 2022) proposed a decentralized controller to find a solution for the leaderless consensus issues with undirected network topology. As a suitable Lyapunov function, the controllers' and systems' total energy is offered to guarantee the closed-loop system stability.…”

Section: Introductionmentioning

confidence: 99%

Robust cooperative multiple flexible-joint arms control using the q-Bernstein-Schurer operators as the uncertainty approximator: A singular perturbation approach

Izadbakhsh

Nazari

Talaei

2022

Journal of Vibration and Control

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The position-force tracking issue of an object being moved by collaborative Multiple Flexible-Joint arms, facing dynamic uncertainties, model nonlinearities, and unknown perturbations is studied in this paper. Toward this end, an adaptive control scheme applying the function approximation technique is suggested, enabling the object to track the reference trajectory. This capability arises from the universal approximation property of the FAT-based approaches. Herein, the q-Bernstein-Schurer operators are exploited to disturbances/uncertain dynamic approximations. Since the parameters of the system are not exactly recognized, adaptive rules are suggested for tuning the coefficients of the operator. The Lyapunov stability analysis guarantees uniformly ultimately bounded stability of all the error signals. Two Flexible-Joint manipulators transporting a rigid object are employed to validate the theoretical achievements. The state-of-the-art Chebyshev Neural Network approximator is also used to compare the suggested methodology. The outcomes exhibit the usefulness of the presented approach, handling the system even in the incidence of uncertainties and disturbances needless to the system’s state feedback for function approximation with a low computational burden.

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Consensus Tracking Control for Multiple Flexible Manipulator Systems Based on RBF Neural Networks

Yi¹,

Liu²,

Miao³

et al. 2022

Lecture Notes in Electrical Engineering

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Distributed Consensus Backstepping Control in Networked Flexible Joint Manipulator Systems

Cited by 4 publications

References 10 publications

Cooperative multiple elastic‐joint arms control using the (p, q)‐analogue of Bernstein operators as the uncertainty approximator

Cooperative multiple elastic‐joint arms control using the (p, q)‐analogue of Bernstein operators as the uncertainty approximator

Robust cooperative multiple flexible-joint arms control using the q-Bernstein-Schurer operators as the uncertainty approximator: A singular perturbation approach

Consensus Tracking Control for Multiple Flexible Manipulator Systems Based on RBF Neural Networks

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