5 papers
Reachability Analysis With Probabilistic Zonotopes: Learning Realized Disturbances and Refining Aleatory Uncertainty
Amir Modares, Zhen Zhang, Themistoklis Charalambous +2
This paper develops a data-driven reachability framework for linear systems whose disturbances are modeled by probabilistic zonotopes (PZs), combining bounded deterministic and Gau…
Data-based Low-conservative Nonlinear Safe Control Learning
Amir Modares, Bahare Kiumarsi, Hamidreza Modares
This paper develops a data-driven safe control framework for nonlinear discrete-time systems with parametric uncertainty and additive disturbances. The proposed approach constructs…
Unifying Direct and Indirect Learning for Safe Control of Linear Systems
Amir Modares, Niyousha Ghiasi, Bahare Kiumarsi +1
This paper develops learning-enabled safe controllers for linear systems subject to system uncertainties and bounded disturbances. Given the disturbance zonotope, the databased clo…
Non-Conservative Data-driven Safe Control Design for Nonlinear Systems with Polyhedral Safe Sets
Amir Modares, Bosen Lian, Hamidreza Modares
This paper presents a data-driven nonlinear safe control design approach for discrete-time systems under parametric uncertainties and additive disturbances. We first characterize a…
Integration of Prior Knowledge into Direct Learning for Safe Control of Linear Systems
Amir Modares, Bahare Kiumarsi, Hamidreza Modares
This paper integrates prior knowledge into direct learning of safe controllers for linear uncertain systems under disturbances. To this end, we characterize the set of all closed-l…