Fault detection and isolation in robotic manipulators and the radial basis function network trained by the Kohonen's self-organizing map

In this work, artificial neural networks are employed in a fault detection and isolation scheme for robotic manipulators. Two networks are utilized: a multilayer perceptron is employed to reproduce the manipulator dynamical behavior, generating a residual vector that is classified by a radial basis function network, giving the fault isolation. Two methods are utilized to choose the radial unit centers in this network. The first method, forward selection, employs subset selection to choose the radial units from the training patterns. The second employs the Kohonen's self-organizing map to fix the radial unit centers in more interesting positions. Simulations employing a two link manipulator and the Puma 560 manipulator indicate that the second method gives a smaller generalization error.