Unit memory repetitive processes and iterative optimal control algorithms

Because of the existence of mixed boundary conditions, the solution of nonlinear dynamic optimal control problems via the maximum principle often requires an algorithm which updates a trial solution from iteration to iteration. This paper analyses this procedure in the form of an unit memory repetitive process. Particular emphasis is given to a novel algorithm for the solution of discrete optimal control problems subject to model-reality differences. Unit memory linear repetitive process theory is employed to analyze the local stability of the algorithm and to show that the technique converges to the correct optimal control solution in spite of model-reality differences.