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pro vyhledávání: '"Völz, Andreas"'
This paper presents the open-source stochastic model predictive control framework GRAMPC-S for nonlinear uncertain systems with chance constraints. It provides several uncertainty propagation methods to predict stochastic moments of the system state
Externí odkaz:
http://arxiv.org/abs/2407.09261
Publikováno v:
2023 62nd IEEE IEEE Conference on Decision and Control (CDC), Singapore, Singapore, December 13 - 15, 2023, pp. 322--327
Trajectory planners of autonomous vehicles usually rely on physical models to predict the vehicle behavior. However, despite their suitability, physical models have some shortcomings. On the one hand, simple models suffer from larger model errors and
Externí odkaz:
http://arxiv.org/abs/2407.06605
This paper presents a distributed model predictive control (DMPC) scheme for nonlinear continuous-time systems. The underlying distributed optimal control problem is cooperatively solved in parallel via a sensitivity-based algorithm. The algorithm is
Externí odkaz:
http://arxiv.org/abs/2406.03134
Publikováno v:
In Mechatronics June 2024 100
The modular open-source framework GRAMPC-D for model predictive control of distributed systems is presented in this paper. The modular concept allows to solve optimal control problems (OCP) in a centralized and distributed fashion using the same prob
Externí odkaz:
http://arxiv.org/abs/2010.12315
Autor:
Landgraf, Daniel, Völz, Andreas, Berkel, Felix, Schmidt, Kevin, Specker, Thomas, Graichen, Knut
Publikováno v:
In Annual Reviews in Control 2023 56
A nonlinear MPC framework is presented that is suitable for dynamical systems with sampling times in the (sub)millisecond range and that allows for an efficient implementation on embedded hardware. The algorithm is based on an augmented Lagrangian fo
Externí odkaz:
http://arxiv.org/abs/1805.01633
Autor:
Landgraf, Daniel *, Völz, Andreas *, Kontes, Georgios, Mutschler, Christopher, Graichen, Knut *
Publikováno v:
In IFAC PapersOnLine 2022 55(20):355-360
Akademický článek
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Publikováno v:
Optimal Control - Applications & Methods; Jul2024, Vol. 45 Issue 4, p1375-1403, 29p