Optimal control with continuous hopfield neural network

Abstract

Based on continuous Hopfield neural network (CHNN), a new alternative is developed for solving linear quadratic (LQ) optimal control problem of discrete-time systems. In this method, the LQ performance index is transformed into the energy function of CHNN, and the control sequence into the output vector of the neurons of CHNN. As a result, solving LQ dynamic optimization problem is equivalent to operating associated CHNN from its initial state to the terminal state. The stable output vector of CHNN represents the optimal control sequence. Because CHNN works in parallel and is of real-time characteristic, the present method is easier to satisfy the requirement of real-time control and will be promising in application.