Global trajectory tracking of a class of manipulators without velocity measurements in random surroundings

Cui, Mingyue; Cui, Yang; Wu, Zhaojing

doi:10.1080/00207179.2021.1957154

Cited by 3 publications

(10 citation statements)

References 35 publications

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“…In order to get closer to the real-world context, we consider that these accelerations are stationary and independent processes. With the help of Lagrange's and relative motion theories, the following random model is obtained [13], [14] M (q) q + C (q, q) q + G (q) = τ + Γ (q) ξ,…”

Section: Preliminaries and Problem Formulation A Problem Formulationmentioning

confidence: 99%

“…Remark 1: So far, random excitation dynamics are canceled in the closed-loop system using the upper bound of the norm of the random excitation matrix [5], [14], [20], [21].…”

Section: Useful Properties Assumptions and Lemmasmentioning

confidence: 99%

“…Cui et al [13] developed a vectorial backstepping for the control of flexible joint electrically driven robot manipulators subjected to mechanical and electrical random disturbances. Cui et al [14] investigated the global output feedback tracking control of robot manipulators in random vibrating environments without velocity measurement. However, all these valuable works proposed torque-based controllers, therefore, cannot been used for robot manipulators with inaccessible torque control interface.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the proposed control strategy can be used for a robot manipulator with a closed and unknown torque control interface. (2) Compared with control methods in [13], [14], the proposed adaptive control method is more accurate and less conservative because no assumption is made about the Frobenius norm of random excitation matrix; instead, a direct adaptive method is used. Moreover, the proposed controller has more substantial applicability since it can be applied for robot manipulators with closed or opened torque control interfaces.…”

Section: Introductionmentioning

confidence: 99%

“…F is used to perform random vibrations rejection[5],[6],[8],[14]. The second approach separates random distur-VOLUME 4, 2016 …”

mentioning

confidence: 99%

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Adaptive Neural Task Space Control for Robot Manipulators With Unknown and Closed Control Architecture Under Random Vibrations

2022

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Robot manipulators are now used in various domains and environments, where they can be subjected to random vibrations. Random vibrations mainly affect the torque control signal, and a torque controller is therefore required to be designed for stabilization purposes. However, for security or intellectual property protection reasons, most commercialized robots are manufactured with unknown and inaccessible torque controller interface such that the user can only design a position/velocity controller. This paper proposes an adaptive task-space velocity controller free from the inner controller's structure and exhibiting stochastic and deterministic disturbances rejection to deal with these issues. To deal with the unknown inner controller, the paper exploits the fact that most torque controllers use a velocity feedback term, and it considers the other terms as an unknown functions vector. To cope with random disturbances, it is demonstrated that the random excitation matrix can be linearly parameterized, and therefore, a direct adaptive method is constructed. Using radial basis function neural network (RBF NN), an indirect adaptive method is developed to cope with deterministic uncertainties. Through Lyapunov theory, the paper proves that all the closed-loop signals are bounded in probability. The effectiveness of the proposed approach is further demonstrated through simulation comparisons.INDEX TERMS Robot manipulators, Adaptive task-space control, Closed inner controller, Random vibrations, Neural networks.

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Section: Preliminaries and Problem Formulation A Problem Formulationmentioning

confidence: 99%

“…Remark 1: So far, random excitation dynamics are canceled in the closed-loop system using the upper bound of the norm of the random excitation matrix [5], [14], [20], [21].…”

Section: Useful Properties Assumptions and Lemmasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…F is used to perform random vibrations rejection[5],[6],[8],[14]. The second approach separates random distur-VOLUME 4, 2016 …”

mentioning

confidence: 99%

See 3 more Smart Citations

Adaptive Neural Task Space Control for Robot Manipulators With Unknown and Closed Control Architecture Under Random Vibrations

2022

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Observer‐based adaptive neural network dynamic surface bipartite containment control for switched fractional order multi‐agent systems

Yuan

Chen

2022

Adaptive Control & Signal

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This article studies the bipartite containment control problem for a class of fractional order nonlinear multi-agent systems in the presence of arbitrary switchings and unmeasured states. Under the framework of Lyapunov function theory, this article proposes an adaptive neural network dynamic surface controller, in which dynamic surface control technology can avoid "explosion of complexity" and obtain fractional derivatives for virtual control functions continuously. Radial basis function neural networks are used to approximate the unknown nonlinear functions and an observer is designed to obtain the unmeasured states. The proposed distributed protocol can ensure all the signals remain semi-global uniformly ultimately bounded in the closed-loop system and all followers can converge to the convex hull containing each leader's trajectory as well as its opposite trajectory different in sign. Example and simulation results confirm the feasibility of the proposed control method.

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Practical trajectory tracking of output constrained random nonlinear systems

Yin

2023

Intl J Robust & Nonlinear

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This article studies practical trajectory tracking of output constrained multi‐input and multi‐output random nonlinear systems. First, the definition of ultimately bounded in the mean square and its Lyapunov criterion are given. Then, by proposing barrier Lyapunov function into the backstepping control design, constraint can be tackled and kept in the given region. Stability analysis is performed to the closed‐loop error system and practical trajectory tracking is achieved, meanwhile the second‐order moment of tracking error can be made arbitrarily small. A simulation example of a mechanical arm demonstrates the effectiveness of this control scheme.

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Global trajectory tracking of a class of manipulators without velocity measurements in random surroundings

Cited by 3 publications

References 35 publications

Adaptive Neural Task Space Control for Robot Manipulators With Unknown and Closed Control Architecture Under Random Vibrations

Adaptive Neural Task Space Control for Robot Manipulators With Unknown and Closed Control Architecture Under Random Vibrations

Observer‐based adaptive neural network dynamic surface bipartite containment control for switched fractional order multi‐agent systems

Practical trajectory tracking of output constrained random nonlinear systems

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