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…
Direct Data Driven Control Using Noisy Measurements
Ramin Esmzad, Gokul S. Sankar, Teawon Han +1
This paper presents a novel direct data-driven control framework for solving the linear quadratic regulator (LQR) under disturbances and noisy state measurements. The system dynami…
Natural Gradient Descent for Control
Ramin Esmzad, Farnaz Adib Yaghmaie, Hamidreza Modares
This paper bridges optimization and control, and presents a novel closed-loop control framework based on natural gradient descent, offering a trajectory-oriented alternative to tra…
Direct Data-Driven Discounted Infinite Horizon Linear Quadratic Regulator with Robustness Guarantees
Ramin Esmzad, Hamidreza Modares
This paper presents a one-shot learning approach with performance and robustness guarantees for the linear quadratic regulator (LQR) control of stochastic linear systems. Even thou…
Trajectory-Oriented Control Using Gradient Descent: An Unconventional Approach
Ramin Esmzad, Hamidreza Modares
In this work, we introduce a novel gradient descent-based approach for optimizing control systems, leveraging a new representation of stable closed-loop dynamics as a function of t…