14 papers
Direct Data Driven Natural Gradient Descent for Control
Ramin Esmzad, Farnaz Adib Yaghmaie, Bahare Kiumarsi +1
This paper introduces a novel direct data-driven control framework based on Natural Gradient Descent (NGD) to design interpretable and robust closed-loop policies without requiring…
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…
Safe Navigation with Zonotopic Tubes: An Elastic Tube-based MPC Framework
Niyousha Ghiasi, Bahare Kiumarsi, Hamidreza Modares
This paper presents an elastic tube-based model predictive control (MPC) framework for unknown discrete-time linear systems subject to disturbances. Unlike most existing elastic tu…
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…
Robust Model Predictive Control Design for Autonomous Vehicles with Perception-based Observers
Nariman Niknejad, Gokul S. Sankar, Bahare Kiumarsi +1
This paper presents a robust model predictive control (MPC) framework that explicitly addresses the non-Gaussian noise inherent in deep learning-based perception modules used for s…