Osborne, Matthew Hewitt (Cranfield University), Shin, Hyo-Sang (Cranfield University), Tsourdos, Antonios (Cranfield University)

A Review of Safe Online Learning for Nonlinear Control Systems

Scheduled for presentation during the Regular Session "Control Design III" (ThC2), Thursday, June 17, 2021, 17:00−17:20, Kozani

2021 International Conference on Unmanned Aircraft Systems (ICUAS), June 15-18, 2021, Athens, Greece

This information is tentative and subject to change. Compiled on April 19, 2024

Abstract

Learning for autonomous dynamic control systems that can adapt to unforeseen environmental changes are of great interest but the realisation of a practical and safe online learning algorithm is incredibly challenging. This paper highlights some of the main approaches for safe online learning of stabilisable nonlinear control systems with a focus on safety certification for stability. We categorise a non-exhaustive list of salient techniques, with a focus on traditional control theory as opposed to reinforcement learning and approximate dynamic programming. This paper also aims to provide a simplified overview of techniques as an introduction to the field. It is the first paper to our knowledge that compares key attributes and advantages of each technique in one paper.