Distributed Formation Control for Multi-Vehicle Systems with Splitting and Merging Capability

Novoth, Szilárd; Zhang, Bin; Ji, Kang; Yu, Dingli

doi:10.1109/lcsys.2020.3002218

Cited by 9 publications

(9 citation statements)

References 18 publications

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“…Various distributed model predictive control (DMPC) approaches have been successfully used in different application areas such as the control of unmanned aerial vehicles, [9][10][11] the enhancement of building automation 12 or the regulation of smart grids. 13 These applications of DMPC are usually based on distributed optimization algorithms, where agents communicate with each other to cooperatively solve the global OCP.…”

Section: Introductionmentioning

confidence: 99%

Asynchronous ADMM for nonlinear continuous‐time systems

Pierer von Esch,

Völz,

Graichen

2024

Optim Control Appl Methods

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This paper presents synchronous as well as asynchronous formulations of the alternating direction method of multipliers (ADMM) for solving continuous‐time nonlinear distributed model predictive control (DMPC) problems. It is shown that the optimal control problems of certain system classes can be transformed to fit the consensus‐based ADMM variant problem formulation. The arising subproblems are solved locally on the agent level while the consensus step is solved centrally by a coordinator. Furthermore, the convergence of the synchronous and asynchronous ADMM algorithms to their respective first‐order optimality conditions is presented in a continuous‐time setting. The algorithm is applied to different example systems for which the convergence behavior and influence of the individual algorithmic parameters are investigated. The computation time of the agents remains unaffected by the system size and thus demonstrates the applicability to high‐scaled systems. Moreover, results show that the asynchronous algorithm performs better in terms of execution time when compared to its synchronous counterpart.

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Section: Introductionmentioning

confidence: 99%

Asynchronous ADMM for nonlinear continuous‐time systems

Pierer von Esch,

Völz,

Graichen

2024

Optim Control Appl Methods

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“…Recently, Burk et al (2021a) tested the performance of an open-source DMPC framework with formation control, including real-world hardware but without truly distributed computation in the experiments. Novoth et al (2021) study more complex scenarios including obstacle avoidance, but without the consideration of hardware experiments and distributed computation. Often in formation control, omnidirectional mobile robots are considered, whereas Rosenfelder et al (2022) tailor a DMPC controller also to mobile robots with nonholonomic constraints.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Distributed MPC via Decentralized Real-Time Optimization: Implementation Results for Robot Formations

Stomberg¹,

Ebel²,

Faulwasser³

et al. 2023

Preprint

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Distributed model predictive control (DMPC) is a flexible and scalable feedback control method applicable to a wide range of systems. While stability analysis of DMPC is quite well understood, there exist only limited implementation results for realistic applications involving distributed computation and networked communication. This article approaches formation control of mobile robots via a cooperative DMPC scheme. We discuss the implementation via decentralized optimization algorithms. To this end, we combine the alternating direction method of multipliers with decentralized sequential quadratic programming to solve the underlying optimal control problem in a decentralized fashion. Our approach only requires coupled subsystems to communicate and does not rely on a central coordinator. Our experimental results showcase the efficacy of DMPC for formation control and they demonstrate the real-time feasibility of the considered algorithms.

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“…Similar work is seen in [4] with collision avoidance and lane‐changing strategy. Splitting and merging are enabled deliberately in [5] to avoid obstacles. However, only simple scenarios are demonstrated in simulation examples, lacking evidence for complicated applications.…”

Section: Introductionmentioning

confidence: 99%

Coordinated collision avoidance for multi‐vehicle systems based on collision time

Wang

Liang

et al. 2021

IET Control Theory & Appl

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Vehicles have irregular shapes and inter-vehicle coordination is not a trivial task. Based on the distributed-system framework, this paper studies multi-vehicle control and coordinated obstacle avoidance for multiple autonomous vehicles with irregular shapes. The goal is to reach target points without collisions. The proposed approaches are based on collision time, which is calculated using vehicles' irregular shapes. The approaches have two parts. The first part enables a number of vehicles to reach the target points. The second part enables collision avoidance, which includes inter-vehicle collisions and vehicle-to-obstacle collisions. Speed regulation approach is proposed to change the speeds, and frequencymodulation approach is proposed to update control commands at varying steps, and a combined approach is also proposed. Simulation examples are set to verify the effectiveness of the proposed approaches.

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Distributed Formation Control for Multi-Vehicle Systems with Splitting and Merging Capability

Cited by 9 publications

References 18 publications

Asynchronous ADMM for nonlinear continuous‐time systems

Asynchronous ADMM for nonlinear continuous‐time systems

Cooperative Distributed MPC via Decentralized Real-Time Optimization: Implementation Results for Robot Formations

Coordinated collision avoidance for multi‐vehicle systems based on collision time

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