A stable neural control scheme using a locally activated neural network has been proposed for a class of nonlinear dynamic systems. The locally activated neural network, for a given input, essentially selects a small subset of the network hidden nodes for output computation using the CMAC-like content addressing mechanism. This network aims to maintain local representations of the system dynamics. Thus, the global control performance in the concerned state space is achieved by the cooperation of many local control efforts and furthermore, real-time control can be facilitated because only a small sized network is involved to control and learn at any given time. The proposed control scheme is composed of two stages: (1) prediction error based learning in which the network attempts to learn the nonlinear basis functions of the plant inverse dynamics by a modified backpropagation learning rule; and (2) tracking error based learning in which the network weights are further fine-tuned using the basis set obtained in (1). This basis set spans the locally partitioned vector space of the system inverse dynamics when the prediction error based learning is achieved within a prescribed error tolerance. For uniform stability, the sliding mode control is introduced as a safety mechanism when the network has not sufficiently learned the plant dynamics yet. With suitable assumptions on the controlled plant, global stability and tracking error convergence proof has been given. Finally, the proposed control scheme is verified with computer simulation.

The industrial robot's dynamic performance is frequently measured by positioning accuracy at high speeds and a good dynamic controller is essential that can accurately compute robot dynamics at a servo rate high enough to ensure system stability. A real-time dynamic controller for an industrial robot is developed here using neural networks. First, an efficient time-selectable hidden layer architecture has been developed based on system dynamics localized in time, which lends itself to real-time learning and control along with enhanced mapping accuracy. Second, the neural network architecture has also been specially tuned to accommodate servo dynamics. This not only facilitates the system design through reduced sensing requirements for the controller but also enhances the control performance over the control architecture neglecting servo dynamics. Experimental results demonstrate the controller's excellent learning and control performances compared with a conventional controller and thus has good potential for practical use in industrial robots.

An indirect learning and direct adaptive control scheme based on neural networks-composite adaptive neural control-is proposed for a class of nonlinear systems. With indirect learning, the neural network learns the nonlinear basis functions of the system inverse dynamics by a modified backpropagation learning rule. The basis set spans the locally partitioned vector space of inverse dynamics with direct adaptation when indirect learning is achieved within a prescribed error tolerance. For localization of the state space of inverse dynamics, the hash addressing technique from CMAC is used for selecting only a small subset of the network hidden nodes according to where the input vector lies. As such, the global control performance can be obtained by the cooperation of many local convergence properties. For uniform stability, sliding mode control is introduced when the neural network has not sufficiently learned the plant dynamics. With suitable assumptions on the controlled plant, global stability and tracking error convergence proof can be given. Finally, the proposed control scheme is verified with computer simulation.>