9309486 Acar As part of this proposal, an optimal controller based on artificial neural networks will be developed for a class of nonlinear systems. The Proposed controller will be capable of adapting and optimizing a receding horizon quadratic cost function in real time for a system which has unknown nonlinearities in its state variables. The complete controller design will consist of various stages of modeling the nonlinearities, estimating future values of the system states, designing a critic for the cost, and learning the optimal control in real time. All these stages of the controller design will involve various types of neural networks which may utilize different forms of supervised learning methods integrated with the control problem. Briefly the objectives of this work are as follows. developing and simulating a real- time optimal controller based on neural networks. Comparing its performance with the (off-line) optimal controller and two other neural network based variations. Obtaining some stability bounds (or attraction regions) of the neural network controlled system experimentally and analytically. Implementing the controller on the two hardware experiments in the Neuro-Controller Laboratory at the University of Missouri-Rolla. ***
