Discrete-time optimal control using neural nets

Abstract

The authors show how neural networks can be incorporated in optimal control strategies by providing a mathematical formulation and numerical algorithms in terms of general gradient descent and backpropagation. They present techniques that use neural nets in nonlinear optimal control. It is shown that D.H. Nguyen and B. Widrow's (1990) self-learning control is a special case of this technique. Control of an inverted pendulum using a neural net in nonlinear feedback is simulated, demonstrating the usefulness of the approach.<>