Neural network reference compensation technique for position control of robot manipulators

A neural network technique for robot manipulator control is proposed. This technique called reference compensation technique(RCT), compensates for uncertainties in robot dynamics at input trajectory level rather than at the joint torque level. The ultimate goal of the proposed technique is to achieve an ideal computed-torque controlled system. Compensating at trajectory level carries several advantages over other neural network control schemes that compensate at robot joint torques. First, the position tracking performance is better. Second, the neural controller is more robust to feedback controller gain variations. Finally, practical implementation can be done with ease without changing the internal control algorithm. Simulation studies have been conducted for various neural network structures and different training signals. The results showed the superior performances of the RCT over other NN control schemes