Adaptive Fuzzy Control of Uncertain Robotic Manipulator

Zhou, Jing; Qunli, Zhang

doi:10.1155/2018/4703492

Cited by 14 publications

(14 citation statements)

References 23 publications

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“…In recent years, many scholars have performed a lot of research on the control of the manipulator. At present, there are many control strategies for the manipulator, such as sliding mode control [5,6], adaptive control [7], fuzzy control [8,9], PID control [10], neural network control [11,12], and decoupling control [13]. Sliding mode control is widely used in manipulator control systems because of its strong robustness.…”

Section: Introductionmentioning

confidence: 99%

Fractional-Order Iterative Sliding Mode Control Based on the Neural Network for Manipulator

Zhang

Wen-bo

2021

Mathematical Problems in Engineering

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This study aimed to improve the position tracking accuracy of the single joint of the manipulator when the manipulator model information is uncertain. The study is based on the theory of fractional calculus, radial basis function (RBF) neural network control, and iterative sliding mode control, and the RBF neural network fractional-order iterative sliding mode control strategy is proposed. First, the stability analysis of the proposed control strategy is carried out through the Lyapunov function. Second, taking the two-joint manipulator as an example, simulation comparison and analysis are carried out with iterative sliding mode control strategy, fractional-order iterative sliding mode reaching law control strategy, and fractional-order iterative sliding mode surface control strategy. Finally, through simulation experiments, the results show that the RBF neural network fractional-order iterative sliding mode control strategy can effectively improve the joints’ tracking and control accuracy, reduce the position tracking error, and effectively suppress the chattering caused by the sliding mode control. It is proved that the proposed control strategy can ensure high-precision position tracking when the information of the manipulator model is uncertain.

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Section: Introductionmentioning

confidence: 99%

Fractional-Order Iterative Sliding Mode Control Based on the Neural Network for Manipulator

Zhang

Wen-bo

2021

Mathematical Problems in Engineering

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“…Therefore, multiple control methods are combined so as to resolve the tracking control problem of the manipulator. [52][53][54] A new method of trajectory tracking control is proposed to improve tracking speed of the manipulator. 55 A finite-time trajectory controller is proposed.…”

Section: Related Workmentioning

confidence: 99%

Manipulator trajectory tracking based on adaptive sliding mode control

Zhao

Tao

et al. 2022

Concurrency and Computation

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A manipulator is a complex electromechanical system that is nonlinear, strongly coupled, and uncertain. Achieving its precise and high-quality trajectory control is difficult.Sliding mode control (SMC) is one of the common control methods for manipulators.However, discontinuities in SMC can cause jitter and vibration in the manipulator system, leading to a reduction in the performance of the control system. For the self-adaptive capability jitter vibration problem of SMC, the Dobot magician manipulator is treated as the research object in this article. The dynamics equations of the manipulator are established by Lagrange method, and a simplified model of the manipulator dynamics is constructed. The method of self-adaptive sliding mode control is proposed. Self-adaptive parameters are added to the SMC to achieve self-adaptive adjustment of the SMC parameters. In the MATLAB/Simulink simulation environment analysis show that the self-adaptive SMC method has better self-tuning ability and trajectory tracking ability than the traditional SMC, and weakens the jitter phenomenon existing in the traditional SMC.

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“…where    m M are positive numbers. in order to use the backstepping theory in designing the controller, external control input u is first introduced into the generalized control torque [27][28]:…”

Section: A Robotic Manipulator Systemmentioning

confidence: 99%