4 papers
Data-Driven Continuous-Time Linear Quadratic Regulator via Closed-Loop and Reinforcement Learning Parameterizations
Armin Gießler, Felix Thömmes, Sören Hohmann
This paper studies data-driven approaches to the continuous-time linear quadratic regulator (LQR) problem based on two existing parameterizations, namely a closed-loop (CL) paramet…
Data-driven Linear Quadratic Integral Control: A Convex Formulation and Policy Gradient Approach
Armin Gießler, Pol Jané-Soneira, Sören Hohmann
This paper studies the data-driven synthesis of linear quadratic integral (LQI) controllers for continuous-time systems. The objective is to achieve optimal state-feedback control…
Bridging Continuous-time LQR and Reinforcement Learning via Gradient Flow of the Bellman Error
Armin Gießler, Albertus Johannes Malan, Sören Hohmann
In this paper, we present a novel method for computing the optimal feedback gain of the infinite-horizon Linear Quadratic Regulator (LQR) problem via an ordinary differential equat…
Dynamic State-Feedback Control for LPV Systems: Ensuring Stability and LQR Performance
Armin Gießler, Felix Strehle, Jochen Illerhaus +1
In this paper, we propose a novel dynamic state-feedback controller for polytopic linear parameter-varying (LPV) systems with constant input matrix. The controller employs a projec…