Positioning error compensation for a parallel robot-based on BP neural networks

The main error sources and the limitations of conventional error compensation for the 6-DOF precision parallel robot were discussed. An error compensation method based on Back Propagation (BP) neural network for the articulatory space of a parallel robot was presented in the local workspace of precision positioning by measuring the end pose. BP neural network model and datum sample of error compensation were established, and the datum sample was standardized. By the experiment, the numbers of node in hidden layer was obtained. In order to improve the generalization performance, the overfitting was prevented in the network training. After error compensation, the positioning error and the orientation error reduced by 80% and 60%, respectively. The experimental results show that the error compensation based on BP neural network has an obvious effect on that of the articulatory space of parallel robot, which satisfies the accuracy requirement of the precision parallel robot.