A New Smoothing Technique for Bang-Bang Optimal Control Problems

TL;DR

This paper introduces a novel bounded smooth function to approximate the sign function in bang-bang control problems, enabling smoother solutions and improved numerical performance in indirect optimal control methods.

Contribution

A new normalized L2-norm smoothing function is proposed to better approximate bang-bang controls, simplifying algorithms and enhancing convergence.

Findings

The proposed method yields smoother control solutions.

Numerical tests show improved convergence and performance.

Applicable to minimal-time and fuel-optimal control problems.

Abstract

Bang-bang control is ubiquitous for Optimal Control Problems (OCPs) where the constrained control variable appears linearly in the dynamics and cost function. Based on the Pontryagin's Minimum Principle, the indirect method is widely used to numerically solve OCPs because it enables to derive the theoretical structure of the optimal control. However, discontinuities in the bang-bang control structure may result in numerical difficulties for gradient-based indirect method. In this case, smoothing or regularization procedures are usually applied to eliminating the discontinuities of bang-bang controls. Traditional smoothing or regularization procedures generally modify the cost function by adding a term depending on a small parameter, or introducing a small error into the state equation. Those procedures may complexify the numerical algorithms or degenerate the convergence performance. To overcome these issues, we propose a bounded smooth function, called normalized L2-norm function, to approximate the sign function in terms of the switching function. The resulting optimal control is smooth and can be readily embedded into the indirect method. Then, the simplicity and improved performance of the proposed method over some existing methods are numerically demonstrated by a minimal-time oscillator problem and a minimal-fuel low-thrust trajectory optimization problem that involves many revolutions.