A new method of solving 6R robot inverse kinematics equations based on dynamic fuzzy neural networks（D-FNN）was presented to improve its accuracy and efficiency. In view of the high-dimensional nonlinearity of 6R robot inverse kinematics equations and the complexity of solving these equations, the D-FNN was improved to fit for multiple-input multiple-output system, and also to establish inverse kinematics solution prediction model of 6R robot. Both position and orientation samples in work space were obtained through forward kinematics and were regarded as input variables of prediction model, the output variables of which were joint angles in joint space. Inverse kinematics solution prediction model was trained by sample data. At last, this prediction model was applied to complex movement trajectory simulation of KR16-2 robot, and the results of the prediction were compared with those of prediction models based on radial basis function (RBF) and back propagation(BP) neural networks. The comparison showed that the D-FNN prediction model of solving 6R robot inverse kinematics equations featured high accuracy, optimal robustness and strong generalization ability, and was proved to be feasible and effective.
