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Specifying and Analyzing Networked and Layered Control Systems Operating on Multiple Clocks
We consider the problem of reasoning about networked and layered control systems using assume-guarantee specifications.
Safe Online Dynamics Learning with Initially Unknown Models and Infeasible Safety Certificates
Our approach requires no prior model and corresponds, to the best of our knowledge, to the first algorithm that guarantees safety in settings with occasionally infeasible safety certificates without requiring a backup non-learning-based controller.
ABC-LMPC: Safe Sample-Based Learning MPC for Stochastic Nonlinear Dynamical Systems with Adjustable Boundary Conditions
Sample-based learning model predictive control (LMPC) strategies have recently attracted attention due to their desirable theoretical properties and their good empirical performance on robotic tasks.
Learning for Safety-Critical Control with Control Barrier Functions
Modern nonlinear control theory seeks to endow systems with properties of stability and safety, and have been deployed successfully in multiple domains.
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