Original scientific paper

https://doi.org/10.17559/TV-20190906094136

A New Computed Torque Control System with an Uncertain RBF Neural Network Controller for a 7-DOF Robot

Liandong Wang* ; School of Mechanical and Aerospace Engineering (SAME), Jilin University, No. 2699 Qianjin Street, Changchun, 130012 China
Xiaoqin Zhou ; School of Mechanical and Aerospace Engineering (SAME), Jilin University, No. 2699 Qianjin Street, Changchun, 130012 China
Tiehua Hu ; China Academy of Machinery Science & Technology (CAM), No. 2 Shouti South Road, Haidian District, Beijing, 100044 China

Abstract

A novel percutaneous puncture robot system is proposed in the paper. Increasing the surgical equipment precision to reduce the patient's pain and the doctor's operation difficulty to treat smaller tumors can increase the success rate of surgery. To attain this goal, an optimized Computed Torque Law (CTL) using a radial basis function (RBF) neural network controller (RCTL) is proposed to improve the direction and position accuracy. BRF neural network with an uncertain term (URBF) which is able to compensate the system error caused by the imprecision of the model is added in the RCTL system. At first, a 7-DOF robotic system is established. It consists of robotic arm and actuator control channels. Now, the RBF compensator is added to the CTL to adjust the robot arm to reduce the position and direction errors. The angle and velocity errors of the robot arm are compensated using the RBF controller. According to the Lyapunov theory, the accuracy of torque control system depends on path tracking errors, inertia of robot, dynamic parameters and disturbance of each joint. Compared to general CTL approaches, the precision of a 7-DOF robot could be improved by adjusting the RBF parameters.

Keywords

compensating controller; computed torque; control system; RBF neural network; robot control

Hrčak ID:

244766

URI

https://hrcak.srce.hr/244766

Publication date:

17.10.2020.

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