Abstract

In this paper the development of a neural controller implemented in a five Degrees Of Freedom (DOF) redundant robot is presented. The design of the control law considers the robotic system inverse model, including the performance of the actuators for the five joints, obtained through a feedforward neural network with backpropagation learning algorithm. This inverse structure is weighted by desired acceleration and derivative proportional feedback loops to provide the appropriate supply voltage to the servo motors of the robotic manipulator. Tracking tests are performed to a path in Cartesian space using a simulator developed using MatLab/Simulink software tools. It assesses the neural controller performance versus classical computed torque controller, comparing the results of curves in the joint space and Cartesian through RMS errors indices of Cartesian and joint positions.