Osborne, MatthewShin, HyosangTsourdos, Antonios2021-07-282021-07-282021-07-19Osborne 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, Athens2575-7296https://doi.org/10.1109/ICUAS51884.2021.9476765https://dspace.lib.cranfield.ac.uk/handle/1826/16941Learning 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.enAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/Nonlinear dynamical systemsReal-time systemsNonlinear control systemsReinforcement learningHeuristic algorithmsConference paper