Tracking Control of Parallel Robot Manipulators Using RBF Neural Network

2021 ◽  
pp. 629-642
Author(s):  
Vu Le Huy ◽  
Nguyen Dinh Dzung
Keyword(s):  
Neural Network ◽  
Tracking Control ◽  
Robot Manipulators ◽  
Rbf Neural Network ◽  
Parallel Robot

Neural Network Trajectory Tracking Control of Compliant Parallel Robot

2015 ◽  
Vol 799-800 ◽  
pp. 1069-1073
Author(s):  
Hao Tian ◽  
Yue Qing Yu
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Control Method ◽  
Rbf Neural Network ◽  
Control Strategies ◽  
Parallel Robot ◽  
Network Control ◽  
Trajectory Tracking Control ◽  
Compliant Joint

Trajectory tracking control of compliant parallel robot is presented. According to the characteristics of compliant joint, the system model is derived and the dynamic equation is obtained based on the Lagrange method. Radial Basis Function (RBF) neural network control is designed to globally approximate the model uncertainties. Further, an itemized approximate RBF control method is proposed for higher identify precision. The trajectory tracking abilities of two control strategies are compared through simulation.

Frequency tracking control of the WPT system based on fuzzy RBF neural network

2021 ◽  
Author(s):  
Yuanyuan Liu ◽  
Fei Liu ◽  
Hongwei Feng ◽  
Guoxin Zhang ◽  
Lu Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Tracking Control ◽  
Rbf Neural Network ◽  
Frequency Tracking

scholarly journals Adaptive PD Control Based on RBF Neural Network for a Wire-Driven Parallel Robot and Prototype Experiments

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Yuqi Wang ◽  
Qi Lin ◽  
Xiaoguang Wang ◽  
Fangui Zhou
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control Method ◽  
Rbf Neural Network ◽  
Approximation Error ◽  
Wind Tunnel Test ◽  
Parallel Robot ◽  
Network Control ◽  
Pd Control ◽  
Control Scheme

An adaptive PD control scheme is proposed for the support system of a wire-driven parallel robot (WDPR) used in a wind tunnel test. The control scheme combines a PD control and an adaptive control based on a radial basis function (RBF) neural network. The PD control is used to track the trajectory of the end effector of the WDPR. The experimental environment, the external disturbances, and other factors result in uncertainties of some parameters for the WDPR; therefore, the RBF neural network control method is used to approximate the parameters. An adaptive control algorithm is developed to reduce the approximation error and improve the robustness and control precision of the WDPR. It is demonstrated that the closed-loop system is stable based on the Lyapunov stability theory. The simulation results show that the proposed control scheme results in a good performance of the WDPR. The experimental results of the prototype experiments show that the WDPR operates on the desired trajectory; the proposed control method is correct and effective, and the experimental error is small and meets the requirements.

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