Costate Estimation and Covector Mapping for Bernstein Polynomial Direct Methods in Optimal Control

Abstract

Bernstein polynomial approximation of continuous functions has a slower rate of convergence as compared to other approximation methods. ``The fact seems to have precluded any numerical application of Bernstein polynomials from having been made. Perhaps they will find application when the properties of the approximant in the large are of more importance than the closeness of the approximation.'' -- has remarked P.J. Davis in his 1963 book Interpolation and Approximation. This paper presents a direct approximation method for nonlinear optimal control problems with mixed input and state constraints based on Bernstein polynomial approximation, in which the dynamic and path constraints are imposed at the Bernstein coefficients (control points). We provide a rigorous analysis showing that, under convergence assumptions on the control polygons, the proposed method yields consistent approximations of time continuous optimal control problems and can be used for costate estimation of the optimal control problems. In particular, multipliers satisfying a relaxed Karush Kuhn Tucker system of the resulting nonlinear program, scaled by the quadrature weight, approximate the costate and the multipliers of the optimal control problem. Owing to the structure of the Bernstein differentiation matrix, the discrete transversality conditions are contained in the relaxed Karush Kuhn Tucker system, and no additional closure conditions need to be added to it. This result leads to the formulation of the Covector Mapping Theorem for Bernstein polynomial approximation.