The problem of stability in the application of neural network to continuous-time dynamic systems

Abstract

Using a neural network to identify a function in the dynamic equation brings about additional difficulties which are not generic in other function approximation problems. First, training samples can not be arbitrarily chosen due to hard nonlinearity, so are apt to be nonuniform over the region of interest. Second, the system may become unstable while attempting to obtain the samples. This paper deals with these problems in continuous-time systems and suggests an effective solution, which provides stability and uniform sampling by the virtue of a supervisory controller. The supervisory control algorithm can be applied to robot system dynamics. The algorithm can be applied to an n-th order robot system, a simulation result is given for a simple two link robot.