A NEURAL-NETWORK COMPENSATOR FOR UNCERTAINTIES IN ROBOTIC ASSEMBLY

It is difficult to represent the nonlinear characteristics in the dynamics of robot manipulators by means of a mathematical model. An alternative approach of using a neural network to learn the parametric and unstructured uncertainties in robot manipulators is proposed. It is then embedded in the structure of a joint torque perturbation observer to compensate for uncertainties in the robot dynamic model. As the result, an accurate estimate of the joint reaction torque against the environment can be deduced. The approach is applied to monitor the insertion force during electronic components assembly using a SCARA robot. A true teaching signal of neural network for learning the model uncertainties is obtained. Furthermore, a special motion test is conducted to generate the required training data set. After learning, the neural network is capable of reproducing the training data. The generalizing ability of the network enables it to output the correct compensation signal for a trajectory which it has not been trained. With the proposed technique, it is possible to verify the success of component insertion in real time and avoid causing damages to the electronic components.