A review of safe online learning for nonlinear control systems

Date

2021-07-19

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Publisher

IEEE

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Type

Conference paper

ISSN

2575-7296

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Citation

Osborne M, Shin H-S, Tsourdos A. (2021) A review of safe online learning for nonlinear control systems. In: 2021 International Conference on Unmanned Aircraft Systems (ICUAS), 15-18 June 2021, Athens

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.

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Software Description

Software Language

Github

Keywords

Nonlinear dynamical systems, Real-time systems, Nonlinear control systems, Reinforcement learning, Heuristic algorithms

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Attribution-NonCommercial 4.0 International

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