Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics

Abstract

This paper focuses on learning a model of system dynamics online while satisfying safety constraints.Our motivation is to avoid offline system identification or hand-specified dynamics models and allowa system to safely and autonomously estimate and adapt its own model during online operation.Given streaming observations of the system state, we use Bayesian learning to obtain a distributionover the system dynamics. In turn, the distribution is used to optimize the system behavior andensure safety with high probability, by specifying a chance constraint over a control barrier function.

arXiv commentTo appear in L4DC 2020. First two authors contributed equally

References in corpus (5)

• Safe Controller Optimization for Quadrotors with Gaussian Processes
• Adaptive Safety with Control Barrier Functions
• Bayesian Learning-Based Adaptive Control for Safety Critical Systems
• Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors
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Cited by in corpus (6)

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• Region-Based Self-Triggered Control for Perturbed and Uncertain Nonlinear Systems
• Safety Under Uncertainty: Tight Bounds with Risk-Aware Control Barrier Functions
• Structured learning of safety guarantees for the control of uncertain dynamical systems
• Guaranteed Trajectory Tracking under Learned Dynamics with Contraction Metrics and Disturbance Estimation
• Combining Control Barrier Functions and Behavior Trees for Multi-Agent Underwater Coverage Missions