Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics

12/20/2019
by   Mohammad Javad Khojasteh, et al.
0

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.

READ FULL TEXT
research
12/29/2020

Control Barriers in Bayesian Learning of System Dynamics

This paper focuses on learning a model of system dynamics online while s...
research
12/22/2021

ProBF: Learning Probabilistic Safety Certificates with Barrier Functions

Safety-critical applications require controllers/policies that can guara...
research
03/23/2023

Rate-Tunable Control Barrier Functions: Methods and Algorithms for Online Adaptation

Control Barrier Functions offer safety certificates by dictating control...
research
08/23/2022

Probabilistic Safe Online Learning with Control Barrier Functions

Learning-based control schemes have recently shown great efficacy perfor...
research
03/29/2021

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

This paper addresses the problem of safety-critical control for systems ...
research
06/24/2021

Comparison between safety methods control barrier function vs. reachability analysis

This report aims to compare two safety methods: control barrier function...
research
09/21/2020

Adaptive Meta-Learning for Identification of Rover-Terrain Dynamics

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

Please sign up or login with your details

Forgot password? Click here to reset