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Found 6 papers, 3 papers with code
Control Barriers in Bayesian Learning of System Dynamics
This paper focuses on learning a model of system dynamics online while satisfying safety constraints.
Learning-based attacks in Cyber-Physical Systems: Exploration, Detection, and Control Cost trade-offs
We study the trade-offs between the information acquired by the attacker from observations, the detection capabilities of the controller, and the control cost.
Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties
Robots operating in real world settings must navigate and maintain safety while interacting with many heterogeneous agents and obstacles.
Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics
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.
Learning-based attacks in cyber-physical systems
In the case of scalar plants, we derive an upper bound on the attacker's deception probability for any measurable control policy when the attacker uses an arbitrary learning algorithm to estimate the system dynamics.
Stabilizing a linear system using phone calls: when time is information
Each symbol transmitted from a sensor to a controller in a closed-loop system is received subject to some to random delay.
