Coordinated Non-Cooperative Distributed Model Predictive Control for Decoupled Systems Using Graphs**Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - GRK 1856.

Alrifaee, Bassam; Heßeler, Frank-Josef; Abel, Dirk

doi:10.1016/j.ifacol.2016.10.399

Cited by 21 publications

(1 citation statement)

References 11 publications

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“…By creating a centralized model of the platoon from the model of one vehicle, the students program a centralized controller for vehicle platooning with MPC. The final part is to distribute the control problem to each vehicle in the platooning with priority-based non-cooperative distributed model predictive control (Alrifaee et al (2016(Alrifaee et al ( , 2015(Alrifaee et al ( , 2013).…”

Section: Control In Networked Vehiclesmentioning

confidence: 99%

Remote Teaching with the Cyber-Physical Mobility Lab

Mokhtarian¹,

Scheffe²,

Alrifaee³

et al. 2022

Preprint

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<p>Self-driving laboratories receive increasing acceptance as they are used in research and education. For research, they mainly serve as a platform on which algorithms can be tested under realistic conditions before getting deployed into the real world. In education, they can add value by creating a reference to reality as they o er an application for theoretical knowledge. In combination with the fact that practical work is well appreciated among students, such setup helps to motivate and thus enhance the learning experience. However, not every institution has the capabilities to create a self-driving lab on its own. Instead, open-source and remotely accessible laboratories, like our Cyber-Physical Mobility Lab (CPM Lab) can be used to engage with the domain of networked and autonomous vehicles. By means of two of our courses, we demonstrate the capabilities of the CPM Lab. These two courses can either be used as groundwork to develop own lectures in this domain, or used directly since the course materials are open-source.</p>

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Section: Control In Networked Vehiclesmentioning

confidence: 99%

Remote Teaching with the Cyber-Physical Mobility Lab

Mokhtarian¹,

Scheffe²,

Alrifaee³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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Designing Maneuver Automata of Motion Primitives for Optimal Cooperative Trajectory Planning

Pedrosa,

Scheffe,

Alrifaee

et al. 2024

Cooperatively Interacting Vehicles

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Trajectory planning techniques form a central step to enable autonomous driving. The motion primitives method generates an automaton of precomputed maneuvers with structure-exploiting properties. Thereby, the trajectory planning problem can be reduced to finding an admissible/optimal sequence of motion primitives. In this chapter, we present ways to designing maneuver automata based on different system models and on either analytical or data-based approaches for automaton generation. Moreover, numerical methods for computing optimal maneuvers are listed and we discuss graph-based planning techniques. A subsequent chapter shows the evaluation of motion primitives automata in the Cyber-Physical Mobility Lab.

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Prioritized Trajectory Planning for Networked Vehicles Using Motion Primitives

Scheffe,

Pedrosa,

Flaßkamp

et al. 2024

Cooperatively Interacting Vehicles

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The computation time required to solve nonconvex, nonlinear optimization problems increases rapidly with their size. This poses a challenge in trajectory planning for multiple networked vehicles with collision avoidance. In the centralized formulation, the optimization problem size increases with the number of vehicles in the networked control system (NCS), rendering the formulation unusable for experiments. We investigate two methods to decrease the complexity of networked trajectory planning. First, we approximate the optimization problem by discretizing the vehicle dynamics with an automaton, which turns it into a graph-search problem. Our search-based trajectory planning algorithm has a limited horizon to further decrease computation complexity. We achieve recursive feasibility by design of the automaton which models the vehicle dynamics. Second, we distribute the optimization problem to the vehicles with prioritized distributed model predictive control (P-DMPC), which reduces the problem size. To counter the incompleteness of P-DMPC, we propose a framework for time-variant priority assignment. The framework expands recursive feasibility to every vehicle in the NCS. We present two time-variant priority assignment algorithms for road vehicles, one to improve vehicle progress and one to improve computation time of the NCS. We evaluate our approach for online trajectory planning of multiple networked vehicles in simulations and experiments.

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Coordinated Non-Cooperative Distributed Model Predictive Control for Decoupled Systems Using Graphs**Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - GRK 1856.

Cited by 21 publications

References 11 publications

Remote Teaching with the Cyber-Physical Mobility Lab

Remote Teaching with the Cyber-Physical Mobility Lab

Designing Maneuver Automata of Motion Primitives for Optimal Cooperative Trajectory Planning

Prioritized Trajectory Planning for Networked Vehicles Using Motion Primitives

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