Symplectic algorithms for stable manifolds in control theory

TL;DR

This paper introduces a symplectic algorithm for computing stable manifolds in control theory, improving convergence and long-term accuracy over traditional methods, demonstrated through an optimal control example.

Contribution

The paper develops a symplectic algorithm with proven convergence estimates that extends local stable manifolds to larger regions, reducing divergence and computational costs.

Findings

Proven convergence radius and error estimates for the algorithm.

Enhanced long-time stability compared to general-purpose schemes.

Successful application to a nonlinear optimal control problem.

Abstract

In this note, we propose a symplectic algorithm for the stable manifolds of the Hamilton-Jacobi equations combined with an iterative procedure in [Sakamoto-van~der Schaft, IEEE Transactions on Automatic Control, 2008]. Our algorithm includes two key aspects. The first one is to prove a precise estimate for radius of convergence and the errors of local approximate stable manifolds. The second one is to extend the local approximate stable manifolds to larger ones by symplectic algorithms which have better long-time behaviors than general-purpose schemes. Our approach avoids the case of divergence of the iterative sequence of approximate stable manifolds, and reduces the computation cost. We illustrate the effectiveness of the algorithm by an optimal control problem with exponential nonlinearity.