Fast-robot system identification based on neural network models

The authors present a system identification scheme and an adaptive control algorithm for a class of nonlinear systems, based on the computational properties of artificial neural network models. An estimation procedure for the link parameters is described in which identification is carried out using the parallel recursive prediction error technique. The algorithm enables the weights in each neuron of the network to be updated in an efficient parallel manner and has better convergence than the classical backpropagation algorithm. The whole of the algorithm can be distributed over a network of parallel processors to achieve impressive speedup. An example is given for the first three links of the Stanford arm to demonstrate the effectiveness of this algorithm for the cases of dense gain matrix and diagonal gain matrix