SOLVING THE FORWARD KINEMATICS PROBLEM OF A PARALLEL KINEMATIC MACHINE USING THE NEURAL NETWORK METHOD

摘要

In this paper, the forward kinematic problem of a 3-DOF asymmetrical parallel kinematic machine has been solved using multi-layered neural network method. The mathematical expression of the multi-layered feed forward neural networks with back-propagation is introduced. Neural network of the 3-DOF parallel manipulator was trained based on the data generated from the closed-form inverse kinematic solution. Levenberg-Marquardt algorithm is applied to train the moderate-sized feed forward neural network, which leads to the fastest convergence of the network. After the learning stage, the network was tested on a random selected data and the simulation results showed that the neural network configuration can receive the expected accuracy of the parallel manipulator. The optimal artificial neural network model of the 3-DOF parallel manipulator has been found with tan-sigmoid transfer function for the middle hidden layer. The trainedrnneural network model of the forward kinematics of the 3-DOF parallel manipulator can be used for real-time control purpose.