24 citations · 69 across the 26 of their papers we have counts for
27 papers
Exploiting Hierarchical Controller Structure in Contextual Parameter Learning for Humanoid Loco-Manipulation
Sebastian Hirt, Lukas Theiner, Jan Peters +1
Hierarchical control architectures are widely used to decompose complex control problems into interacting control levels and are particularly important in robotics, where planning,…
Uncertainty-Aware Fusion for Resilient Distributed Radar Sensing
Christian Eckrich, Maik Pfefferkorn, Rolf Findeisen +2
Distributed radar sensing enables safe and resilient operation in mobile robotic and vehicular systems by combining multiple local views of the scene. In this paper, we consider a…
Real-time Gaussian Process based Approximate Model Predictive Trajectory Tracking Control for Autonomous Vehicles
Alexander Rose, Lukas Theiner, Rolf Findeisen
Applying model predictive control on embedded systems remains challenging due to the high computational cost of solving optimal control problems. To address this limitation, comput…
Bridging Data-Driven Reachability Analysis and Statistical Estimation via Constrained Matrix Convex Generators
Peng Xie, Zhen Zhang, Rolf Findeisen +1
Data-driven reachability analysis enables safety verification when first-principles models are unavailable. This requires constructing sets of system models consistent with measure…
Data-Driven Reachability Analysis with Optimal Input Design
Peng Xie, Davide M. Raimondo, Rolf Findeisen +1
This paper addresses the conservatism in data-driven reachability analysis for discrete-time linear systems subject to bounded process noise, where the system matrices are unknown…
Efficient Controller Learning from Human Preferences and Numerical Data Via Multi-Modal Surrogate Models
Lukas Theiner, Maik Pfefferkorn, Yongpeng Zhao +2
Tuning control policies manually to meet high-level objectives is often time-consuming. Bayesian optimization provides a data-efficient framework for automating this process using…
High-Dimensional Surrogate Modeling for Closed-Loop Learning of Neural-Network-Parameterized Model Predictive Control
Sebastian Hirt, Valentinus Suwanto, Hendrik Alsmeier +2
Learning controller parameters from closed-loop data has been shown to improve closed-loop performance. Bayesian optimization, a widely used black-box and sample-efficient learning…
Real-Time Non-Smooth MPC for Switching Systems: Application to a Three-Tank Process
Hendrik Alsmeier, Felix Häusser, Andreas Knödler +4
Real-time model predictive control of non-smooth switching systems remains challenging due to discontinuities and the presence of discrete modes, which complicate numerical integra…
Bang-Ride Optimal Control: Monotonicity, External Positivity, and Fast Battery Charging
Shengling Shi, Jacob Sass, Jiaen Wu +5
This work studies a class of optimal control problems with scalar inputs and general constraints, whose solutions follow a bang-ride pattern that always activates a constraint and…
A Hierarchical Surrogate Model for Efficient Multi-Task Parameter Learning in Closed-Loop Control
Sebastian Hirt, Lukas Theiner, Maik Pfefferkorn +1
Many control problems require repeated tuning and adaptation of controllers across distinct closed-loop tasks, where data efficiency and adaptability are critical. We propose a hie…
Efficient Learning of Vehicle Controller Parameters via Multi-Fidelity Bayesian Optimization: From Simulation to Experiment
Yongpeng Zhao, Maik Pfefferkorn, Maximilian Templer +1
Parameter tuning for vehicle controllers remains a costly and time-intensive challenge in automotive development. Traditional approaches rely on extensive real-world testing, makin…
Exploiting Prior Knowledge in Preferential Learning of Individualized Autonomous Vehicle Driving Styles
Lukas Theiner, Sebastian Hirt, Alexander Steinke +1
Trajectory planning for automated vehicles commonly employs optimization over a moving horizon - Model Predictive Control - where the cost function critically influences the result…
Imitation Learning of MPC with Neural Networks: Error Guarantees and Sparsification
Hendrik Alsmeier, Lukas Theiner, Anton Savchenko +2
This paper presents a framework for bounding the approximation error in imitation model predictive controllers utilizing neural networks. Leveraging the Lipschitz properties of the…
Time-Series-Informed Closed-loop Learning for Sequential Decision Making and Control
Sebastian Hirt, Lukas Theiner, Rolf Findeisen
Closed-loop performance of sequential decision making algorithms, such as model predictive control, depends strongly on the choice of controller parameters. Bayesian optimization a…
Interpretation of High-Dimensional Regression Coefficients by Comparison with Linearized Compressing Features
Joachim Schaeffer, Jinwook Rhyu, Robin Droop +2
Linear regression is often deemed inherently interpretable; however, challenges arise for high-dimensional data. We focus on further understanding how linear regression approximate…
Probabilistically Input-to-State Stable Stochastic Model Predictive Control
Maik Pfefferkorn, Rolf Findeisen
Employing model predictive control to systems with unbounded, stochastic disturbances poses the challenge of guaranteeing safety, i.e., repeated feasibility and stability of the cl…
Safe Learning-Based Optimization of Model Predictive Control: Application to Battery Fast-Charging
Sebastian Hirt, Andreas Höhl, Johannes Pohlodek +4
Model predictive control (MPC) is a powerful tool for controlling complex nonlinear systems under constraints, but often struggles with model uncertainties and the design of suitab…
Safe and High-Performance Learning of Model Predicitve Control using Kernel-Based Interpolation
Alexander Rose, Philipp Schaub, Rolf Findeisen
We present a method that allows efficient and safe approximation of model predictive controllers using kernel interpolation. Since the computational complexity of the approximating…
Safe and Stable Closed-Loop Learning for Neural-Network-Supported Model Predictive Control
Sebastian Hirt, Maik Pfefferkorn, Rolf Findeisen
Safe learning of control policies remains challenging, both in optimal control and reinforcement learning. In this article, we consider safe learning of parametrized predictive con…
Neural Horizon Model Predictive Control -- Increasing Computational Efficiency with Neural Networks
Hendrik Alsmeier, Anton Savchenko, Rolf Findeisen
The expansion in automation of increasingly fast applications and low-power edge devices poses a particular challenge for optimization based control algorithms, like model predicti…
Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field data
Joachim Schaeffer, Eric Lenz, Duncan Gulla +3
Health monitoring, fault analysis, and detection are critical for the safe and sustainable operation of battery systems. We apply Gaussian process resistance models on lithium iron…
Accounting for the Effects of Probabilistic Uncertainty During Fast Charging of Lithium-ion Batteries
Minsu Kim, Joachim Schaeffer, Marc D. Berliner +3
Batteries are nonlinear dynamical systems that can be modeled by Porous Electrode Theory models. The aim of optimal fast charging is to reduce the charging time while keeping batte…
Stability-informed Bayesian Optimization for MPC Cost Function Learning
Sebastian Hirt, Maik Pfefferkorn, Ali Mesbah +1
Designing predictive controllers towards optimal closed-loop performance while maintaining safety and stability is challenging. This work explores closed-loop learning for predicti…
Learning Model Predictive Control Parameters via Bayesian Optimization for Battery Fast Charging
Sebastian Hirt, Andreas Höhl, Joachim Schaeffer +3
Tuning parameters in model predictive control (MPC) presents significant challenges, particularly when there is a notable discrepancy between the controller's predictions and the a…