1. A method for approximation of optimal control for nonlinear discrete time systems comprising the steps of: obtaining state variables from a system model, iteratively generating control sequences to optimize desired control variables for subsequent training a recurrent neural network, the value of each said control variable being independent of the other control variables, mapping said control variables onto the recurrent neural network implemented by a processor.