IDENTIFICATION AND DECENTRALIZED ADAPTIVE-CONTROL USING DYNAMICAL NEURAL NETWORKS WITH APPLICATION TO ROBOTIC MANIPULATORS

Efficient implementation of a neural network-based strategy for the on-line adaptive control of complex dynamical systems characterized by an interconnection of several subsystems (possibly nonlinear) centers on the rapidity of the convergence of the training scheme used for learning the system dynamics. For illustration, in order to achieve a satisfactory control of a multijointed robotic manipulator during the execution of high speed trajectory tracking tasks, the highly nonlinear and coupled dynamics together with the variations in the parameters necessitate a fast updating of the control actions. For facilitating this requirement, a new multilayer neural network structure that includes dynamical nodes in the hidden layer is proposed and a supervised learning scheme that employs a simple distributed updating rule is used for the on-line identification and decentralized adaptive control. Important characteristic features of the resulting control scheme are discussed and a quantitative evaluation of its performance in the illustrative example of tracking of desired motions by a robotic manipulator is given.