Adaptive Fuzzy Finite-Time Command-Filtered Backstepping Control of Flexible-Joint Robots

Datouo, Roger; Ahanda, Joseph Jean-Baptiste Mvogo; Melingui, Achille; Biya-Motto, Frédéric; Zobo, Bernard Essimbi

doi:10.1017/s0263574720000910

Cited by 12 publications

(13 citation statements)

References 49 publications

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“…Nevertheless, we should notice that the feedback gains K dii are chosen significantly enough to reduce the settling time and the tracking error for most torque controllers. Therefore, for most practical cases, K dii ≫ 1 2 such that the matrices K d and K d − 1 2 I n×n are positive definite [22], [23]. The latter means that for all y ∈ R n , the following inequalities hold: (59) From ( 56) and (57), and the fact that inertia matrix M (q) is positive definite, for all y ∈ R n , there exist a positive constant ρ 3 that fulfills the following three constraints (63)…”

Section: Appendix B Proof Of Theoremmentioning

confidence: 99%

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: Appendix B Proof Of Theoremmentioning

confidence: 99%

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

2022

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“…In summary, the adaptive control methods heavily use Lyapunov method for stability purpose [24][25][26]. Although adaptive control with Lyapunov method may provide desired results, but in case of rare event it fails.…”

Section: Related Literature Reviewmentioning

confidence: 99%

Analysis of exit probability for a trajectory tracking robot in case of a rare event

et al. 2021

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In this paper, a novel statistical application of large deviation principle (LDP) to the robot trajectory tracking problem is presented. The exit probability of the trajectory from stability zone is evaluated, in the presence of small-amplitude Gaussian and Poisson noise. Afterward, the limit of the partition function for the average tracking error energy is derived by solving a fourth-order system of Euler–Lagrange equations. Stability and computational complexity of the proposed approach is investigated to show the superiority over the Lyapunov method. Finally, the proposed algorithm is validated by Monte Carlo simulations and on the commercially available Omni bundleTM robot.

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“…Recently, a number of backstepping adaptive fuzzy controllers have been designed subject to the flexible robotic manipulator [10][11][12][13][14][15][16][17][18][19][20]. In [18], by transforming the single-link flexible robotic manipulator system with unknown internal dynamics into an uncertain fifth-order nonlinear system, a backstepping adaptive fuzzy controller was developed for the fifth-order transformation model.…”

Section: Introductionmentioning

confidence: 99%

“…Along another key line of research, as a result the limit of the network bandwidth in practice, plenty of adaptive sampled data control approaches have been developed [20][21][22][23][24][25][26]. Rather than the normal adaptive fuzzy control method in [10][11][12][13][14][15][16][17][18][19][20], the difficulty of sampling control is mainly reflected in two aspects. Firstly, in order to save more network bandwidth, the network will transmit data after a fixed sampling period, which means the backstepping sampled data control method can only use the information at the sampling time.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Control for Flexible Robotic Manipulator with a Fixed Sampled Period

Zhang

Dai

2022

Electronics

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In this paper, a backstepping sampled data control method is developed for a flexible robotic manipulator whose internal dynamic is completely unknown. To address the internal uncertainties, the fuzzy logical system (FLS) is considered. Moreover, considering the limited network bandwidth, the designed controller and adaptive laws only contain the sampled data with a fixed sampled period. By invoking the Lyapunov stability theory, all signals of the flexible robotic manipulator are semi-global uniformly ultimately bounded (SGUUB). Ultimately, an application to a flexible robotic manipulator is given to verify the validity of the sampled data controller.

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Adaptive Fuzzy Finite-Time Command-Filtered Backstepping Control of Flexible-Joint Robots

Cited by 12 publications

References 49 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

Analysis of exit probability for a trajectory tracking robot in case of a rare event

Adaptive Fuzzy Control for Flexible Robotic Manipulator with a Fixed Sampled Period

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