A Gauss-Newton Method for ODE Optimal Tracking Control

TL;DR

This paper develops a continuous optimization method using Gauss-Newton iterations for solving ODE-based optimal tracking control problems, incorporating regularization and constraints, and demonstrates its effectiveness through numerical experiments.

Contribution

It introduces a novel Gauss-Newton based iterative framework for infinite-dimensional optimal control problems with ODEs, combining linearization and function space techniques.

Findings

Effective convergence demonstrated in numerical experiments

Linear surrogate models simplify complex control problems

Method accommodates regularization and box-constraints

Abstract

This paper introduces and analyses a continuous optimization approach to solve optimal control problems involving ordinary differential equations (ODEs) and tracking type objectives. Our aim is to determine control or input functions, and potentially uncertain model parameters, for a dynamical system described by an ODE. We establish the mathematical framework and define the optimal control problem with a tracking functional, incorporating regularization terms and box-constraints for model parameters and input functions. Treating the problem as an infinite-dimensional optimization problem, we employ a Gauss-Newton method within a suitable function space framework. This leads to an iterative process where, at each step, we solve a linearization of the problem by considering a linear surrogate model around the current solution estimate. The resulting linear auxiliary problem resembles a linear-quadratic ODE optimal tracking control problem, which we tackle using either a gradient descent method in function spaces or a Riccati-based approach. Finally, we present and analyze the efficacy of our method through numerical experiments.