5 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…
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…
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…
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…