Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization

• URL: http://arxiv.org/abs/2005.05888v2
• Date: Fri, 25 Jun 2021 16:05:51 GMT
• Title: Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization
• Authors: Ankush Chakrabarty and Mouhacine Benosman
• Abstract summary: In this paper, a modular design methodology is formulated, that consists of three design phases. The potential of our proposed learning-based observer is demonstrated on a benchmark nonlinear system.
• Score: 4.184419714263417
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data generated from dynamical systems with unknown dynamics enable the learning of state observers that are: robust to modeling error, computationally tractable to design, and capable of operating with guaranteed performance. In this paper, a modular design methodology is formulated, that consists of three design phases: (i) an initial robust observer design that enables one to learn the dynamics without allowing the state estimation error to diverge (hence, safe); (ii) a learning phase wherein the unmodeled components are estimated using Bayesian optimization and Gaussian processes; and, (iii) a re-design phase that leverages the learned dynamics to improve convergence rate of the state estimation error. The potential of our proposed learning-based observer is demonstrated on a benchmark nonlinear system. Additionally, certificates of guaranteed estimation performance are provided.

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