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