Data-driven abstraction-based control synthesis
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Publication:6551651
DOI10.1016/j.nahs.2024.101467zbMath1541.93106MaRDI QIDQ6551651
Unnamed Author, Ben Wooding, Rupak Majumdar, Sadegh Soudjani, Unnamed Author
Publication date: 7 June 2024
Published in: Nonlinear Analysis. Hybrid Systems (Search for Journal in Brave)
Cites Work
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- Estimation of the Lipschitz constant of a function
- Formal methods for discrete-time dynamical systems
- Automated verification and synthesis of stochastic hybrid systems: a survey
- Formal synthesis of closed-form sampled-data controllers for nonlinear continuous-time systems under STL specifications
- \textsc{DryVR}: data-driven verification and compositional reasoning for automotive systems
- Probabilistic reachability and safety for controlled discrete time stochastic hybrid systems
- Temporal logic guided safe model-based reinforcement learning: a hybrid systems approach
- Performance Bounds for the Scenario Approach and an Extension to a Class of Non-Convex Programs
- SCOTS
- Verification and Control of Hybrid Systems
- Formal Guarantees in Data-Driven Model Identification and Control Synthesis
- An Information Global Optimization Algorithm with Local Tuning
- Approximately Bisimilar Symbolic Models for Incrementally Stable Switched Systems
- Acceleration of Global Search by Implementing Dual Estimates for Lipschitz Constant
- The Scenario Approach to Robust Control Design
- Approximation Metrics for Discrete and Continuous Systems
- Feedback Refinement Relations for the Synthesis of Symbolic Controllers
- Robust Control for Dynamical Systems with Non-Gaussian Noise via Formal Abstractions
- Model-based reinforcement learning for approximate optimal control with temporal logic specifications
- Data-driven verification and synthesis of stochastic systems via barrier certificates
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