2 citations · 2 across the 4 of their papers we have counts for
4 papers
Multi-Phase Optimal Control Problems for Efficient Nonlinear Model Predictive Control with acados
Jonathan Frey, Katrin Baumgärtner, Gianluca Frison +1
Computationally efficient nonlinear model predictive control relies on elaborate discrete-time optimal control problem (OCP) formulations trading off accuracy with respect to the c…
AC4MPC: Actor-Critic Reinforcement Learning for Nonlinear Model Predictive Control
Rudolf Reiter, Andrea Ghezzi, Katrin Baumgärtner +3
\Ac{MPC} and \ac{RL} are two powerful control strategies with, arguably, complementary advantages. In this work, we show how actor-critic \ac{RL} techniques can be leveraged to imp…
Progressive Smoothing for Motion Planning in Real-Time NMPC
Rudolf Reiter, Katrin Baumgärtner, Rien Quirynen +1
Nonlinear model predictive control (NMPC) is a popular strategy for solving motion planning problems, including obstacle avoidance constraints, in autonomous driving applications.…
Gauss-Newton Runge-Kutta Integration for Efficient Discretization of Optimal Control Problems with Long Horizons and Least-Squares Costs
Jonathan Frey, Katrin Baumgärtner, Moritz Diehl
This work proposes an efficient treatment of continuous-time optimal control problem (OCP) with long horizons and nonlinear least-squares costs. The Gauss-Newton Runge-Kutta (GNRK)…