Data-Driven Modeling and Distributed Predictive Control of Mixed Vehicle Platoons

Zhan, Jingyuan; Ma, Zibo; Zhang, Liguo

doi:10.1109/tiv.2022.3168591

Cited by 42 publications

(21 citation statements)

References 21 publications

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“…For computation, all the experiments are run in MATLAB 2021a. In centralized DeeP-LCC, the quadprog solver is utilized to solve (21) via the interior point method with the optimality tolerances set to 10 −3 . In the distributed DeeP-LCC algorithm, no solvers are needed for computation, and the absolute and relative tolerances for the stopping criterion in Appendix B are set to δ abs = 0.1, δ rel = 10 −3 .…”

Section: Methodsmentioning

confidence: 99%

“…Remark 1. DeeP-LCC requires a centralized cloud unit to collect data (12) and u ini , ini , y ini of the entire mixed traffic system and assign control inputs for all the CAVs via solving the centralized optimization problem (21). Both traffic simulations [22] and real-world tests [25] have validated its potential in mitigating traffic waves in a moderatescale setup.…”

Section: Centralized Formulation Of Deep-lccmentioning

confidence: 99%

“…Both traffic simulations [22] and real-world tests [25] have validated its potential in mitigating traffic waves in a moderatescale setup. In larger-scale traffic flow, however, the computation time in solving (21) would soon become intolerable for real-time implementation, and the communication constraints and flexible structures of mixed traffic patterns would also limit the practical application.…”

Section: Centralized Formulation Of Deep-lccmentioning

confidence: 99%

“…where σ yi ∈ R (1+mi)Tini , i ∈ N n 1 are slack variables to ensure feasibility, and λ yi , λ gi > 0, i ∈ N n 1 are weight coefficients for regularization. Then, similarly to the final centralized DeeP-LCC formulation (21), the following regularized version of cooperative DeeP-LCC is provided Cooperative DeeP-LCC:…”

Section: Final Design For Cooperative Deep-lccmentioning

confidence: 99%

See 3 more Smart Citations

Distributed Deep-Lcc for Cooperatively Smoothing Large-Scale Mixed Traffic Flow Via Connected and Automated Vehicles

Wang

Lian²,

Jiang³

et al. 2022

SSRN Journal

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Section: Methodsmentioning

confidence: 99%

Section: Centralized Formulation Of Deep-lccmentioning

confidence: 99%

Section: Centralized Formulation Of Deep-lccmentioning

confidence: 99%

Section: Final Design For Cooperative Deep-lccmentioning

confidence: 99%

See 2 more Smart Citations

Distributed Deep-Lcc for Cooperatively Smoothing Large-Scale Mixed Traffic Flow Via Connected and Automated Vehicles

Wang

Lian²,

Jiang³

et al. 2022

SSRN Journal

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“…In [43], authors proposed and compared centralized and decentralized MPC formulations for controlling the UAVs' maneuvers when carrying a payload. In [44], a strategy with centralized and distributed MPC algorithms was addressing the problem of controlling platoons with both autonomous and human-driven vehicles, and in [45] distributed and centralized MPC formulations are also studied for tracking multiple targets using a swarm of UAVs. Centralized schemes were proposed in [46]- [48], for controlling platoons based on leader's behavior, for placing UAVs to form a mesh network, and for preventing potential collisions between multiple UAVs navigating in a narrow area, respectively.…”

Section: Introductionmentioning

confidence: 99%

E-CNMPC: Edge-Based Centralized Nonlinear Model Predictive Control for Multiagent Robotic Systems

et al. 2022

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With the wide deployment of autonomous multi-agent robotic systems, control solutions based on centralized algorithms have been developed. Even though these centralized algorithms can optimize the performance of the multi-agent robotic systems, they require a lot of computational effort, and a centralized unit to undertake the whole process. Yet, many robotic platforms like some ground robots and even more, aerial robots, do not have the computing capacity to execute this kind of frameworks on their on onboard computers. While cloud computing has been used as a solution for offloading computationally demanding robotic applications, from the robots to the cloud servers, the latency they introduce to the system has set them unsuitable for time sensitive applications. To overcome these challenges, this article promotes an Edge computing-based Centralized Nonlinear Model Predictive Control (E-CNMPC) framework to control, and optimize, in swarm formation, the trajectory of multiple ground robotic agents, while taking under consideration potential collisions. The data processing procedure for the time critical application of controlling the robots in a centralized manner, is offloaded to the edge machine, thus the framework benefits from the provided edge resources, features, and centralized optimal performance, while the latency remains bounded in desired values. Besides, real experiments were conducted as a proof-of-concept of the proposed framework to evaluate the system's performance and effectiveness. INDEX TERMSEdge-based Centralized Nonlinear Model Predictive Control (E-CNMPC), Edge Computing, Kubernetes, Robotics

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Observer‐based output feedback event‐triggered robust control for heterogeneous vehicular platoon systems

Tian,

Li,

Tong

2024

Intl J Robust & Nonlinear

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In this article, the problem of output feedback robust control for heterogeneous vehicle platoon system (HVPS) is investigated. A novel double‐channel event‐triggered mechanism is designed to reduce the waste of communication resources and unnecessary data transmissions. A neural network state observer is designed by intermittent output signal to estimate unmeasurable states. The prescribed performance function is used to establish a strong string stability condition. Combining backstepping recursive technique and prescribed performance function with its inverse transformation, observer‐based robust adaptive output feedback event‐triggered platoon controller is designed, which can ensure the individual stability, string stability, and strong string stability of HVPS. It is proved that all signals are bounded by Lyapunov stability analysis. Finally, the validity of the proposed results is proved by simulation.

show abstract

Data-Driven Modeling and Distributed Predictive Control of Mixed Vehicle Platoons

Cited by 42 publications

References 21 publications

Distributed Deep-Lcc for Cooperatively Smoothing Large-Scale Mixed Traffic Flow Via Connected and Automated Vehicles

Distributed Deep-Lcc for Cooperatively Smoothing Large-Scale Mixed Traffic Flow Via Connected and Automated Vehicles

E-CNMPC: Edge-Based Centralized Nonlinear Model Predictive Control for Multiagent Robotic Systems

Observer‐based output feedback event‐triggered robust control for heterogeneous vehicular platoon systems

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