Iterative Optimal Control Syntheses for Nonlinear Systems in Constrained Environments

This paper presents computational solutions for nonlinear optimal control problems with general state-space constraints that may not be representable in terms of analytical expressions. The presented approaches hinge on the iterative nature of an underlying computational nonlinear optimal control methodology by which the non-analytical constraint description can be incorporated as quadratic constraints within each iteration. The functionality and efficiency of the proposed methods are discussed from a computational point of view and illustrated on a standard parallel parking problem.