Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics
Mohammad Javad Khojasteh, Vikas Dhiman, Massimo Franceschetti, Nikolay Atanasov
2020Learning for Dynamics and Control28 citations
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.
Topics & Concepts
Constraint (computer-aided design)Computer scienceSystem dynamicsProbabilistic logicDynamics (music)Identification (biology)Function (biology)Bayesian probabilityOnline modelArtificial intelligenceEngineeringMathematicsMechanical engineeringBiologyAcousticsStatisticsBotanyEvolutionary biologyPhysicsAnomaly Detection Techniques and ApplicationsFault Detection and Control SystemsData Stream Mining Techniques