Title :
Robot inverse dynamic control with feedback and compensation based on neural network
Author :
Yuming, Tan ; Zongyuan, Mao
Author_Institution :
Dept. of Autom., South China Univ. of Technol., Guangzhou, China
Abstract :
The paper deals with an inverse dynamic control method for the robot. The scheme is based on the neural network and so can be applied even without the knowledge of the robot model. With the feedback and compensation, the robot presents rather good characters in tracking precision and anti interference performance. Furthermore, the neural network learning times are drastically reduced without affecting the control precision. The different computer simulation results which all used the PUMA500 robot as an example are compared and analyzed
Keywords :
compensation; feedback; intelligent control; neurocontrollers; robot dynamics; PUMA500 robot; anti interference performance; compensation; computer simulation results; feedback; learning times; neural network; robot inverse dynamic control; robot model; tracking precision; Acceleration; Automatic control; Computer simulation; Error correction; Feedback; Force feedback; Interference; Neural networks; Neurofeedback; Nonlinear dynamical systems; Robot control; Robotics and automation;
Conference_Titel :
Intelligent Processing Systems, 1997. ICIPS '97. 1997 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-4253-4
DOI :
10.1109/ICIPS.1997.669207
