Neural networks for learning inverse kinematics of redundant manipulators

Summary form only given, as follows. A feedforward neural network was used to solve the problem of inverse kinematics for the redundant robots. A learning algorithm was also developed for the training of the network. The convergence of the training process was guaranteed according to Liapunov´s stability theory. Moreover, the speed of training can be increased by increasing a learning rate parameter. Simulation was done to illustrate the effectiveness of the proposed network