A NEURAL-NETWORK COMPENSATOR FOR UNCERTAINTIES IN ROBOTIC ASSEMBLY

It is difficult to represent the nonlinear characteristics in the dyna mics of robot manipulators by means of a mathematical model. An altern ative approach of using a neural network to learn the parametric and u nstructured uncertainties in robot manipulators is proposed. It is the n embedded in the structure of a joint torque perturbation observer to compensate for uncertainties in the robot dynamic model. As the resul t, an accurate estimate of the joint reaction torque against the envir onment can be deduced. The approach is applied to monitor the insertio n force during electronic components assembly using a SCARA robot. A t rue teaching signal of neural network for learning the model uncertain ties is obtained. Furthermore, a special motion test is conducted to g enerate the required training data set. After learning, the neural net work is capable of reproducing the training data. The generalizing abi lity of the network enables it to output the correct compensation sign al for a trajectory which it has not been trained. With the proposed t echnique, it is possible to verify the success of component insertion in real time and avoid causing damages to the electronic components.