Zobrazeno 1 - 10
of 597
pro vyhledávání: '"Wollherr, A."'
In this work, an adaptive predictive control scheme for linear systems with unknown parameters and bounded additive disturbances is proposed. In contrast to related adaptive control approaches that robustly consider the parametric uncertainty, the pr
Externí odkaz:
http://arxiv.org/abs/2409.01955
Trajectory planning for autonomous driving is challenging because the unknown future motion of traffic participants must be accounted for, yielding large uncertainty. Stochastic Model Predictive Control (SMPC)-based planners provide non-conservative
Externí odkaz:
http://arxiv.org/abs/2406.13396
Publikováno v:
IEEE Transactions on Automatic Control, 2024
Handling uncertainty in model predictive control comes with various challenges, especially when considering state constraints under uncertainty. Most methods focus on either the conservative approach of robustly accounting for uncertainty or allowing
Externí odkaz:
http://arxiv.org/abs/2402.10538
In automated driving, predicting and accommodating the uncertain future motion of other traffic participants is challenging, especially in unstructured environments in which the high-level intention of traffic participants is difficult to predict. Se
Externí odkaz:
http://arxiv.org/abs/2402.00697
We present a stochastic constrained output-feedback data-driven predictive control scheme for linear time-invariant systems subject to bounded additive disturbances. The approach uses data-driven predictors based on an extension of Willems' fundament
Externí odkaz:
http://arxiv.org/abs/2402.00681
Vertical farming allows for year-round cultivation of a variety of crops, overcoming environmental limitations and ensuring food security. This closed and highly controlled system allows the plants to grow in optimal conditions, so that they reach ma
Externí odkaz:
http://arxiv.org/abs/2309.07540
Autor:
Fink, Michael, Daniels, Annalena, Qian, Cheng, Velásquez, Víctor Martínez, Salotra, Sahil, Wollherr, Dirk
As global demand for efficiency in agriculture rises, there is a growing interest in high-precision farming practices. Particularly greenhouses play a critical role in ensuring a year-round supply of fresh produce. In order to maximize efficiency and
Externí odkaz:
http://arxiv.org/abs/2308.06031
The fundamental lemma from behavioral systems theory yields a data-driven non-parametric system representation that has shown great potential for the data-efficient control of unknown linear and weakly nonlinear systems, even in the presence of measu
Externí odkaz:
http://arxiv.org/abs/2304.03386
In this work, we exploit an offline-sampling based strategy for the constrained data-driven predictive control of an unknown linear system subject to random measurement noise. The strategy uses only past measured, potentially noisy data in a non-para
Externí odkaz:
http://arxiv.org/abs/2304.03088
Autor:
Zhang, Zengjie, Wollherr, Dirk
This paper proposes a novel observer-based disturbance estimation method for high degree-of-freedom Euler-Lagrangian systems using an unknown input-output (UIO) sliding mode observer (SMO). Different from the previous SMO methods, this approach does
Externí odkaz:
http://arxiv.org/abs/2303.04008