Adaptive bias RBF neural network control for a robotic manipulator

Liu, Qiong; Li, Dongyu; Ge, Shuzhi Sam; Ji, Ruihang; Ouyang, Zhong; Tee, Keng Peng

doi:10.1016/j.neucom.2021.03.033

Cited by 68 publications

(26 citation statements)

References 34 publications

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“…In [ 28 ], a robust controller based on an RBF neural network was designed to improve the trajectory tracking performance of a 3-DOF robot manipulator. In [ 29 ], an adaptive controller based on an RBF neural network was designed to solve the dynamic deviation problem of a 2-DOF robot manipulator. In [ 30 ], a sliding mode controller was designed to shorten the circular trajectory error of the 3-DOF robot manipulator.…”

Section: Related Workmentioning

confidence: 99%

Trajectory Planning of Robot Manipulator Based on RBF Neural Network

Song

Bai

et al. 2021

Entropy

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Robot manipulator trajectory planning is one of the core robot technologies, and the design of controllers can improve the trajectory accuracy of manipulators. However, most of the controllers designed at this stage have not been able to effectively solve the nonlinearity and uncertainty problems of the high degree of freedom manipulators. In order to overcome these problems and improve the trajectory performance of the high degree of freedom manipulators, a manipulator trajectory planning method based on a radial basis function (RBF) neural network is proposed in this work. Firstly, a 6-DOF robot experimental platform was designed and built. Secondly, the overall manipulator trajectory planning framework was designed, which included manipulator kinematics and dynamics and a quintic polynomial interpolation algorithm. Then, an adaptive robust controller based on an RBF neural network was designed to deal with the nonlinearity and uncertainty problems, and Lyapunov theory was used to ensure the stability of the manipulator control system and the convergence of the tracking error. Finally, to test the method, a simulation and experiment were carried out. The simulation results showed that the proposed method improved the response and tracking performance to a certain extent, reduced the adjustment time and chattering, and ensured the smooth operation of the manipulator in the course of trajectory planning. The experimental results verified the effectiveness and feasibility of the method proposed in this paper.

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Section: Related Workmentioning

confidence: 99%

Trajectory Planning of Robot Manipulator Based on RBF Neural Network

Song

Bai

et al. 2021

Entropy

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“…In the early days, some scholars used the linear control method of state feedback linearization and decomposition of control rate. However, the linear control methods need to establish a linearized mathematical model, and the better choice is to use some nonlinear schemes of control, such as sliding mode variable structure control(SMVC), adaptive method, backstepping technique, Fuzzy logic control(FLC), neural network control(NNC), etc [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Adaptive RBF Neural Network Backstepping Control for Two-Link Robot Manipulators

Wang

2022

J. Phys.: Conf. Ser.

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In this paper, an adaptive radial basis function neural network(RBFNN) backstepping controller is presented for a two-link robot manipulator in the presence of uncertainty and external interferences. RBFNNs are applied to approximate uncertain nonlinear functions, and considering the backstepping technique, an adaptive RBFNN backstepping control strategy is proposed. It is proved by the Lyapunov function that tracking errors converge to a small neighborhood of the equilibrium point and the closed-loop system variables are bounded. The simulation results demonstrate the effectiveness of the presented design method.

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“…e dynamic control is designed according to the dynamic characteristics of robot manipulators, so it can make the control quality of the system better [1,2]. At present, the commonly used dynamic control methods mainly include intelligent PID control [3][4][5], iterative learning control [6][7][8][9], adaptive neural network control [10][11][12][13], sliding mode control [14][15][16], and active disturbance rejection control [17,18]. Aiming at ndegree-of-freedom rigid robots, Hernández-Guzmán and Orrante-Sakanassi [4] proposed a control scheme for directdrive brushless direct-current (BLDC) motors, which solved the position control problem of n direct-drive BLDC with complex, nonlinear, and highly coupled mechanical loads.…”

Section: Introductionmentioning

confidence: 99%

Modified Linear Active Disturbance Rejection Control for Uncertain Robot Manipulator Trajectory Tracking

Xiao

Shen

2021

Mathematical Problems in Engineering

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To solve the problems of model uncertainties, dynamic coupling, and external disturbances, a modified linear active disturbance rejection controller (MLADRC) is proposed for the trajectory tracking control of robot manipulators. In the computer simulation, MLADRC is compared to the proportional-derivative (PD) controller and the regular linear active disturbance rejection controller (LADRC) for performance tests. Multiple uncertain factors such as friction, parameter perturbation, and external disturbance are sequentially added to the system to simulate an actual robot manipulator system. Besides, a two-degree-of-freedom (2-DOF) manipulator is constructed to verify the control performance of the MLADRC. Compared with the regular LADRC, MLADRC is significantly characterized by the addition of feedforward control of reference angular acceleration, which helps robot manipulators keep up with target trajectories more accurately. The simulation and experimental results demonstrate the superiority of the MLADRC over the regular LADRC for the trajectory tracking control.

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Adaptive bias RBF neural network control for a robotic manipulator

Cited by 68 publications

References 34 publications

Trajectory Planning of Robot Manipulator Based on RBF Neural Network

Trajectory Planning of Robot Manipulator Based on RBF Neural Network

Adaptive RBF Neural Network Backstepping Control for Two-Link Robot Manipulators

Modified Linear Active Disturbance Rejection Control for Uncertain Robot Manipulator Trajectory Tracking

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