8 citations · 14 across the 6 of their papers we have counts for
6 papers
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.…
Fast Generation of Feasible Trajectories in Direct Optimal Control
David Kiessling, Katrin Baumgärtner, Jonathan Frey +3
This paper examines the question of finding feasible points to discrete-time optimal control problems. The optimization problem of finding a feasible trajectory is transcribed to a…
Fourth-order suboptimality of nominal model predictive control in the presence of uncertainty
Florian Messerer, Katrin Baumgärtner, Sergio Lucia +1
We investigate the suboptimality resulting from the application of nominal model predictive control (MPC) to a nonlinear discrete time stochastic system. The suboptimality is defin…
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)…
A dual-control effect preserving formulation for nonlinear output-feedback stochastic model predictive control with constraints
Florian Messerer, Katrin Baumgärtner, Moritz Diehl
We propose a formulation for approximate constrained nonlinear output-feedback stochastic model predictive control. Starting from the ideal but intractable stochastic optimal contr…