on Communication, Control, and Computing

Lately, there has been an extensive interest in the possible uses of neural networks for nonlinear system identification and control. In this paper, we provide a framework for the simultaneous identification and control of a class of unknown, uncertain nonlinear systems. The identification portion relies on modeling the system by a neural network which is trained via a local variant of the Extended Kalman Filter. We will discuss this local algorithm for training a neural network to approximate a nonlinear feedback system. We also give a dynamic programming-based method of deriving near optimal control inputs for the real plant based on this approximation and a measure of its error (covariance). Finally, we combine these methods in a hierarchical algorithm for identification and control of a class of uncertain, unknown systems. The complexity of the whole algorithm is analyzed. 0