Trajectory tracking control of robot manipulator based on RBF neural network and fuzzy sliding mode

Wang, Fei; Chao, Zhiqiang; Huang, Lianbing; Li, Huaying; Zhang, Chuanqing

doi:10.1007/s10586-017-1538-4

Cited by 64 publications

(34 citation statements)

References 13 publications

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“…In [25], the RBF neural network implements self-feedback control, accurate prediction, and real-time control of reasonable data. It has improved tracking accuracy and estimated unmodeled dynamics and external interference issues in [26]. As more and more academic researchers understand the approximation characteristics of RBF, they add RBF neural networks to various fields to study the dynamic characteristics of different systems.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Supervision Control Strategy for Inverted Pendulum Tracking Control

Gao

et al. 2021

Discrete Dynamics in Nature and Society

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This paper presents several control methods and realizes the stable tracking for the inverted pendulum system. Based on the advantages of RBF and traditional PID, a novel PID controller based on the RBF neural network supervision control method (PID-RBF) is proposed. This method realizes the adaptive adjustment of the stable tracking signal of the system. Furthermore, an improved PID controller based on RBF neural network supervision control strategy (IPID-RBF) is presented. This control strategy adopts the supervision control method of feed-forward and feedback. The response speed of the system is further improved, and the overshoot of the tracking signal is further reduced. The tracking control simulation of the inverted pendulum system under three different signals is given to illustrate the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Neural Network Supervision Control Strategy for Inverted Pendulum Tracking Control

Gao

et al. 2021

Discrete Dynamics in Nature and Society

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“…Another approach is to relax the linear parameterized assumption and the requirements of system knowledge, thus, NNs have been used as function approximators. In time series modeling, RBFNN is commonly used for function approximation, since its value is different from zero in infinite space, and its approximation can avoid the local minimum (Wang et al, 2019a ). An RBFNN uses a Gauss function as its activation function.…”

Section: Introductionmentioning

confidence: 99%

PD Control Compensation Based on a Cascade Neural Network Applied to a Robot Manipulator

et al. 2020

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A Proportional Integral Derivative (PID) controller is commonly used to carry out tasks like position tracking in the industrial robot manipulator controller; however, over time, the PID integral gain generates degradation within the controller, which then produces reduced stability and bandwidth. A proportional derivative (PD) controller has been proposed to deal with the increase in integral gain but is limited if gravity is not compensated for. In practice, the dynamic system non-linearities frequently are unknown or hard to obtain. Adaptive controllers are online schemes that are used to deal with systems that present non-linear and uncertainties dynamics. Adaptive controller use measured data of system trajectory in order to learn and compensate the uncertainties and external disturbances. However, these techniques can adopt more efficient learning methods in order to improve their performance. In this work, a nominal control law is used to achieve a sub-optimal performance, and a scheme based on a cascade neural network is implemented to act as a non-linear compensation whose task is to improve upon the performance of the nominal controller. The main contributions of this work are neural compensation based on a cascade neural networks and the function to update the weights of neural network used. The algorithm is implemented using radial basis function neural networks and a recompense function that leads longer traces for an identification problem. A two-degree-of-freedom robot manipulator is proposed to validate the proposed scheme and compare it with conventional PD control compensation.

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“…A considerable number of researchers combined neural networks or fuzzy systems with other control methods and exploited neural networks and fuzzy systems to approximate and compensate time-varying disturbances and internal friction. In [33], an RBF-based fuzzy sliding-mode control method was proposed to make the manipulator track the given trajectory at an ideal dynamic quality. [34] presented a novel adaptive non-singular terminal sliding mode controller for the trajectory tracking of robotic manipulators using radial basis function neural networks (RBFNNs).…”

Section: Introductionmentioning

confidence: 99%

Terminal Traction Control of Teleoperation Manipulator With Random Jitter Disturbance Based on Active Disturbance Rejection Sliding Mode Control

Feng³

2020

IEEE Access

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Trajectory tracking control of robot manipulator based on RBF neural network and fuzzy sliding mode

Cited by 64 publications

References 13 publications

Neural Network Supervision Control Strategy for Inverted Pendulum Tracking Control

Neural Network Supervision Control Strategy for Inverted Pendulum Tracking Control

PD Control Compensation Based on a Cascade Neural Network Applied to a Robot Manipulator

Terminal Traction Control of Teleoperation Manipulator With Random Jitter Disturbance Based on Active Disturbance Rejection Sliding Mode Control

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