Abstract
In this paper, we show that recently developed neural network methods for quadratic programming can be put to use in solving discrete time optimal control problems, with general pointwise constraints on states and controls. We describe a high performance recurrent neural network for a discrete time linear quadratic regulator problem with mixed state–control constraints. The equilibrium point of the proposed model is proved to be equivalent to the optimal solution of the discrete time problem. It is also shown that the proposed network model is stable in the Lyapunov sense and it is globally convergent to an exact optimal solution of the original problem. Several practical examples are provided to show the feasibility and the efficiency of the scheme.
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