Robust optimal control using dynamic programming and guaranteed Euler's method

TL;DR

This paper presents a robust optimal control method using dynamic programming combined with a guaranteed Euler integration approach, providing solutions that are both optimal and robust against disturbances, with practical validation on a numerical example.

Contribution

It extends set-based dynamic programming methods with a guaranteed Euler scheme to improve robustness and solution quality in optimal control problems.

Findings

The method yields robust optimal controls that handle uncertainties.

It outperforms convex optimization-based methods in low-dimensional systems.

A variant inspired by Model Predictive Control offers faster solutions at the cost of robustness.

Abstract

Set-based integration methods allow to prove properties of differential systems, which take into account bounded disturbances. The systems (either time-discrete, time-continuous or hybrid) satisfying such properties are said to be "robust". In the context of optimal control synthesis, the set-based methods are generally extensions of numerical optimal methods of two classes: first, methods based on convex optimization; second, methods based on the dynamic programming principle. Heymann et al. have recently shown that, for certain systems of low dimension, the second numerical method can give better solutions than the first one. They have built a solver (Bocop) that implements both numerical methods. We show in this paper that a set-based extension of a method of the second class which uses a guaranteed Euler integration method, allows us to find such good solutions. Besides, these solutions enjoy the property of robustness against uncertainties on initial conditions and bounded disturbances. We demonstrate the practical interest of our method on an example taken from the numerical Bocop solver. We also give a variant of our method, inspired by the method of Model Predictive Control, that allows us to find more efficiently an optimal control at the price of losing robustness.