Tracking control of robot using hybrid controller based on neural network and computed torque

In order to improve robot manipulator's tracking accuracy, a hybrid controller consisting of a functional link neural network sub-controller (FLNNC) and a computed torque sub-controller (CTC) was introduced into the manipulator, which made use of CTC to drive the manipulator reaching its desired position roughly while employed the FLNNC to compensate the tracking error caused by the dynamic uncertainty and disturbance of the robot. To accomplish this, firstly, a nominal dynamic model of the manipulator was established, and the dynamic uncertainty of the robot manipulator was modeled and formulized. And then, a control system with two close loops was built for the manipulator, and the computed-torque control law based on the nominal manipulator model was planned for the system. Moreover, a functional link neural network (FLNN) being capable of approximating the dynamic uncertainty term of the robot was designed in the system, and the weight learning algorithm for the FLNN was derived. Finally, simulations were made on that system so as to validate the hybrid controller. The results showed that both the position error and speed tracking error of the robot joints could be controlled within ±0.001 rad and±0.001 rad/s, which meant that the proposed hybrid controller was able to make the robots tracking desired trajectory with high precision.