Trajectory Tracking Control Based on RBF Neural Network of The Lower Limb Rehabilitation Robot

2020 ◽  
Author(s):  
Jia Shi ◽  
Linsen Xu ◽  
Gaoxin Cheng ◽  
Jiajun Xu ◽  
Shouqi Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Lower Limb ◽  
Tracking Control ◽  
Rbf Neural Network ◽  
Rehabilitation Robot ◽  
Trajectory Tracking Control ◽  
Limb Rehabilitation ◽  
Lower Limb Rehabilitation

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.

A New Neural Network Based Sliding Mode Adaptive Controller for Tracking Control of Robot Manipulator

2021 ◽  
Vol 11 (2) ◽  
pp. 12-16
Author(s):  
Monisha Pathak ◽  
◽  
Mrinal Buragohain ◽  
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Rbf Neural Network ◽  
Adaptive Controller ◽  
Robotic Manipulator ◽  
Trajectory Tracking Control ◽  
Chattering Effect

In this paper a New RBF Neural Network based Sliding Mode Adaptive Controller (NNNSMAC) for Robot Manipulator trajectory tracking in the presence of uncertainties and disturbances is introduced. The research offers a learning with minimal parameter (LMP) technique for robotic manipulator trajectory tracking. The technique decreases the online adaptive parameters number in the RBF Neural Network to only one, lowering computational costs and boosting real-time performance. The RBFNN analyses the system's hidden non-linearities, and its weight value parameters are updated online using adaptive laws to control the nonlinear system's output to track a specific trajectory. The RBF model is used to create a Lyapunov function-based adaptive control law. The effectiveness of the designed NNNSMAC is demonstrated by simulation results of trajectory tracking control of a 2 dof Robotic Manipulator. The chattering effect has been significantly reduced.

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