Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics

by   Mohammad Javad Khojasteh, et al.

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 allow a 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 distribution over the system dynamics. In turn, the distribution is used to optimize the system behavior and ensure safety with high probability, by specifying a chance constraint over a control barrier function.


Control Barriers in Bayesian Learning of System Dynamics

This paper focuses on learning a model of system dynamics online while s...

ProBF: Learning Probabilistic Safety Certificates with Barrier Functions

Safety-critical applications require controllers/policies that can guara...

Probabilistic Safe Online Learning with Control Barrier Functions

Learning-based control schemes have recently shown great efficacy perfor...

Event-Triggered Safety-Critical Control for Systems with Unknown Dynamics

This paper addresses the problem of safety-critical control for systems ...

Bayesian Learning-Based Adaptive Control for Safety Critical Systems

Deep learning has enjoyed much recent success, and applying state-of-the...

Comparison between safety methods control barrier function vs. reachability analysis

This report aims to compare two safety methods: control barrier function...

Adaptive Meta-Learning for Identification of Rover-Terrain Dynamics

Rovers require knowledge of terrain to plan trajectories that maximize s...