Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Brüdigam, Tim"'
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
Solving chance-constrained stochastic optimal control problems is a significant challenge in control. This is because no analytical solutions exist for up to a handful of special cases. A common and computationally efficient approach for tackling cha
Externí odkaz:
http://arxiv.org/abs/2310.02942
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
Combining efficient and safe control for safety-critical systems is challenging. Robust methods may be overly conservative, whereas probabilistic controllers require a trade-off between efficiency and safety. In this work, we propose a safety algorit
Externí odkaz:
http://arxiv.org/abs/2204.06207
A powerful result from behavioral systems theory known as the fundamental lemma allows for predictive control akin to Model Predictive Control (MPC) for linear time invariant (LTI) systems with unknown dynamics purely from data. While most of data-dr
Externí odkaz:
http://arxiv.org/abs/2112.04439
Horizon length and model accuracy are defining factors when designing a Model Predictive Controller. While long horizons and detailed models have a positive effect on control performance, computational complexity increases. As predictions become less
Externí odkaz:
http://arxiv.org/abs/2108.08014
Trajectory planning in urban automated driving is challenging because of the high uncertainty resulting from the unknown future motion of other traffic participants. Robust approaches guarantee safety, but tend to result in overly conservative motion
Externí odkaz:
http://arxiv.org/abs/2107.00529
Model Predictive Control (MPC) has shown to be a successful method for many applications that require control. Especially in the presence of prediction uncertainty, various types of MPC offer robust or efficient control system behavior. For modeling,
Externí odkaz:
http://arxiv.org/abs/2106.08463
A fundamental aspect of racing is overtaking other race cars. Whereas previous research on autonomous racing has majorly focused on lap-time optimization, here, we propose a method to plan overtaking maneuvers in autonomous racing. A Gaussian process
Externí odkaz:
http://arxiv.org/abs/2105.12236
Autor:
Brüdigam, Tim
This brief introduction to Model Predictive Control specifically addresses stochastic Model Predictive Control, where probabilistic constraints are considered. A simple linear system subject to uncertainty serves as an example. The Matlab code for th
Externí odkaz:
http://arxiv.org/abs/2101.12020