Motion Planning and Adaptive Neural Tracking Control of an Uncertain Two-Link Rigid–Flexible Manipulator With Vibration Amplitude Constraint

Meng, Qingxin; Lai, Xuzhi; Yan, Ze; Su, Chun‐Yi; Wu, Min

doi:10.1109/tnnls.2021.3054611

Cited by 82 publications

(36 citation statements)

References 63 publications

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“…It is well known that the requirement for increasing productivity by high performance robots which can be partly quantified by a high-speed operation, 1 high-end position accuracy, 2 and dexterous manipulation 3 has triggered a huge amount of attention in various multidisciplinary areas, such as humanoid, flexible aircraft wing, rehabilitation exoskeleton robot, 4,5 and so forth. The rigid-flexible robotic system is made up of a rigid link and a flexible link driven via the joint motor.…”

Section: Introductionmentioning

confidence: 99%

“…It is generally known that the accuracy of the dynamic model obtained from the analytical formulation is highly dependent on the flexible deflection and two joint angulars. The coupling effects between the flexible body dynamic and rigid body dynamics for the hybrid ODEs-PDEs systems are certainly different from the reduced-order assumed mode method (AMM), 3,6,7 which resulted in all high-frequency modes of the system are reserved. As such, the traditional control method in rigid robotic and pure flexible robotic cannot be directly utilized for the rigid-flexible robotic.…”

Section: Introductionmentioning

confidence: 99%

“…Distinguished from similar recent investigations, the dynamic equation of rigid-flexible robotic systems is first derived over the vertical plane with the form of coupled ODEs-PDEs. 3,27 Hence, the corresponding FTC problem with unknown boundary control direction is different from rigid robotic and pure flexible robotic systems. 3,22,27 In this study, the developed robust adaptive FTC laws, which are constructed by the Lyapunov direct approach and gravity compensation, are directly designed for an infinite-dimension system.…”

Section: Introductionmentioning

confidence: 99%

“…3,27 Hence, the corresponding FTC problem with unknown boundary control direction is different from rigid robotic and pure flexible robotic systems. 3,22,27 In this study, the developed robust adaptive FTC laws, which are constructed by the Lyapunov direct approach and gravity compensation, are directly designed for an infinite-dimension system. Compared with the existing boundary control methods under the actuator fault, 22,28 this work adds RBF neural networks enhanced with gravity compensation and Nussbaum function to cope with the unknown control direction, which can effectively eliminate the deformation of the flexible link.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robust adaptive fault‐tolerant control using RBF‐based neural network for a rigid‐flexible robotic system with unknown control direction

Wang

Zhou

Tian

2021

Intl J Robust & Nonlinear

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In this article, a radial basis function (RBF)-based robust adaptive fault-tolerant control (FTC) is developed for a class of rigid-flexible robotic systems under actuator failures, unknown control direction, and uncertain external disturbances. The dynamics of the rigid-flexible robotic over a vertical plane is governed by the hybrid ordinary differential equations-partial differential equations. The uncertain external disturbance is estimated via an RBF neural network. Meanwhile, the robust adaptive FTC law is utilized to follow the given joint angular and attenuate the vibration of the flexible structure in the case of actuator failures and unknown distributed disturbances. To handle the unknown control direction, the Nussbaum function is employed to robust FTC laws. By virtue of the Lyapunov direct approach, the trajectory tracking and vibration elimination for the controlled rigid-flexible robotic system are proved and uniformly bounded in face of bounded external disturbances and unknown control directions. The efficacy of the developed control strategy is also illustrated via four comparison examples.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust adaptive fault‐tolerant control using RBF‐based neural network for a rigid‐flexible robotic system with unknown control direction

Wang

Zhou

Tian

2021

Intl J Robust & Nonlinear

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“…In order to avoid undesirable chattering effects, a thin boundary layer neighbouring the switching surface can be adopted, but it usually spoils the tracking performance, leading to a steady-state control error [17]. It has already been shown [12][13][14][15][16] that the tracking error in flexible link manipulators can be reduced by combining ANN with SMC. However, it should be noted that despite ANN's ability to approximate the dynamics of flexible link manipulators and to improve the tracking performance, its architecture must be chosen very carefully, in order to avoid computational complexity and time-consuming issues related to large networks [15].…”

mentioning

confidence: 99%

Intelligent control of a single‐link flexible manipulator using sliding modes and artificial neural networks

Lima

Porto

Oliveira

et al. 2021

Electronics Letters

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This letter presents a new intelligent control scheme for the accurate trajectory tracking of flexible link manipulators. The proposed approach is mainly based on a sliding mode controller for underactuated systems with an embedded artificial neural network to deal with modelling inaccuracies. The adopted neural network only needs a single input and one hidden layer, which drastically reduces the computational complexity of the control law and allows its implementation in low-power microcontrollers. Online learning, rather than supervised offline training, is chosen to allow the weights of the neural network to be adjusted in real time during the tracking. Therefore, the resulting controller is able to cope with the underactuating issues and to adapt itself by learning from experience, which grants the capacity to deal with plant dynamics properly. The boundedness and convergence properties of the tracking error are proved by evoking Barbalat's lemma in a Lyapunov-like stability analysis. Experimental results obtained with a small single-link flexible manipulator show the efficacy of the proposed control scheme, even in the presence of a high level of uncertainty and noisy signals.

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Manipulator trajectory tracking based on adaptive fuzzy sliding mode control

Zhao

Tao

et al. 2023

Concurrency and Computation

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Sliding mode control is one of the common control methods for manipulators, but the discontinuity of sliding mode control can cause jitter and vibration of the manipulator system. This article takes the Dobot Magician manipulator as the research object and constructs a simplified model of the manipulator dynamics. And the adaptive fuzzy sliding mode control method is designed in combination with fuzzy control theory, which effectively solves the jitter problem of the control torque. In the MATLAB/Simulink simulation environment analysis show that the proposed adaptive fuzzy sliding mode control method solves the jitter problem in sliding mode control with good stability, robustness and tracking performance. By combining the adaptive fuzzy sliding mode control method with the manipulator and comparing the motion time of the same motion trajectory with the PID control method in Dobot Studio, the hardware experiment results effectively verify the feasibility and effectiveness of the designed control method.

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Motion Planning and Adaptive Neural Tracking Control of an Uncertain Two-Link Rigid–Flexible Manipulator With Vibration Amplitude Constraint

Cited by 82 publications

References 63 publications

Robust adaptive fault‐tolerant control using RBF‐based neural network for a rigid‐flexible robotic system with unknown control direction

Robust adaptive fault‐tolerant control using RBF‐based neural network for a rigid‐flexible robotic system with unknown control direction

Intelligent control of a single‐link flexible manipulator using sliding modes and artificial neural networks

Manipulator trajectory tracking based on adaptive fuzzy sliding mode control

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