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

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.

Trajectory Planning of Robot Manipulator Based on RBF Neural Network

“…The input layer is composed of signal source nodes and transmits input excitation to the hidden layer; the hidden layer adopts gauss radial basis functions to map the low-dimensional input to the high-dimensional space and performs curve fitting; the output layer adopts a linear transformation function to perform weighted evaluation on the hidden layer signal in order to obtain the output value. In the RBF network structure, the following notations are used [39]:…”