Zobrazeno 1 - 10
of 4 438
pro vyhledávání: '"Findeisen, A."'
Closed-loop performance of sequential decision making algorithms, such as model predictive control, depends strongly on the parameters of cost functions, models, and constraints. Bayesian optimization is a common approach to learning these parameters
Externí odkaz:
http://arxiv.org/abs/2412.02423
Linear regression is often deemed inherently interpretable; however, challenges arise for high-dimensional data. We focus on further understanding how linear regression approximates nonlinear responses from high-dimensional functional data, motivated
Externí odkaz:
http://arxiv.org/abs/2411.12060
Autor:
Pfefferkorn, Maik, Findeisen, Rolf
Employing model predictive control to systems with unbounded, stochastic disturbances poses the challenge of guaranteeing safety, i.e., repeated feasibility and stability of the closed-loop system. Especially, there are no strict repeated feasibility
Externí odkaz:
http://arxiv.org/abs/2410.08186
We present a method, which allows efficient and safe approximation of model predictive controllers using kernel interpolation. Since the computational complexity of the approximating function scales linearly with the number of data points, we propose
Externí odkaz:
http://arxiv.org/abs/2410.06771
Autor:
Hirt, Sebastian, Höhl, Andreas, Pohlodek, Johannes, Schaeffer, Joachim, Pfefferkorn, Maik, Braatz, Richard D., Findeisen, Rolf
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 suitable cost functions. To address these challenges, we discuss an approach
Externí odkaz:
http://arxiv.org/abs/2410.04982
Safe learning of control policies remains challenging, both in optimal control and reinforcement learning. In this article, we consider safe learning of parametrized predictive controllers that operate with incomplete information about the underlying
Externí odkaz:
http://arxiv.org/abs/2409.10171
The expansion in automation of increasingly fast applications and low-power edge devices poses a particular challenge for optimization based control algorithms, like model predictive control. Our proposed machine-learning supported approach addresses
Externí odkaz:
http://arxiv.org/abs/2408.09781
Autor:
Schaeffer, Joachim, Lenz, Eric, Gulla, Duncan, Bazant, Martin Z., Braatz, Richard D., Findeisen, Rolf
Publikováno v:
Cell Reports Physical Science, 102258 (2024)
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 phosphate battery field data to effectively separate the time-depende
Externí odkaz:
http://arxiv.org/abs/2406.19015
Autor:
Kim, Minsu, Schaeffer, Joachim, Berliner, Marc D., Sagnier, Berta Pedret, Findeisen, Rolf, Braatz, Richard D.
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 battery degradation low. Most past studies assume that model parameters and
Externí odkaz:
http://arxiv.org/abs/2405.01681
Designing predictive controllers towards optimal closed-loop performance while maintaining safety and stability is challenging. This work explores closed-loop learning for predictive control parameters under imperfect information while considering cl
Externí odkaz:
http://arxiv.org/abs/2404.12187