Data-Driven Robust Predictive Control for Mixed Vehicle Platoons Using Noisy Measurement

Lan, Jianglin; Zhao, Dezong

doi:10.1109/tits.2021.3128406

Cited by 37 publications

(30 citation statements)

References 39 publications

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“…We directly use measurable driving data as system output since the HDVs' equilibrium spacing is typically not measurable. This issue of unknown equilibrium spacing has been neglected in many recent studies on mixed traffic [12], [15], [20], [21], [23], [33]. We further show that the linearized mixed traffic system is not controllable (except that the first vehicle is a CAV), but is stabilizable and observable.…”

Section: B Contributionsmentioning

confidence: 83%

“…On the other hand, model predictive control (MPC) has been widely recognized as a primary tool to address control problems with constraints [17], [22]. Recent advancements in data-driven MPC have further provided techniques towards safe learning-based control using measurable data [23]- [25]. One promising method is the Data EnablEd Predictive Control (DeePC) [25] that is able to achieve safe and optimal control for unknown systems using input/output measurements.…”

Section: A Model-based and Model-free Control Of Cavsmentioning

confidence: 99%

“…Remark 1 (State-feedback versus output-feedback): Most existing work on CAV control relies on state feedback which assumes an known equilibrium spacing s * and requires the system state x(t) in (7) (see, e.g., the model-based strategies [6], [15], [33] and the data-driven strategies [20], [21], [23]). The output-feedback case has been less investigated (two notable exceptions are [12], [39]).…”

Section: Linearized State-space Model Of Mixed Traffic Systemmentioning

confidence: 99%

“…Our first experiment (Experiment A) aims to validate the performance of the proposed DeeP-LCC with the standard output-feedback MPC (23) that requires an accurate model. The dynamical model for all the HDVs is set to follow the nominal parameter values [6], [12], [15]: α = 0.6, β = 0.9, v max = 30, s st = 5, s go = 35, v * = 15.…”

Section: A Performance Validation: Comparison With Standard Mpcmentioning

confidence: 99%

“…Despite its well-recognized effectiveness, one crucial challenge for the standard MPC (23) is the requirement of an explicit parametric model (17), which is necessary in estimating the initial state x(t) and predicting future system behaviors. By contrast, DeePC (22) focuses on the data-centric nonparametric representation and bypasses the state estimation procedure [25].…”

mentioning

confidence: 99%

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DeeP-LCC: Data-EnablEd Predictive Leading Cruise Control in Mixed Traffic Flow

Wang¹,

Zheng²,

Li³

et al. 2022

Preprint

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For the control of connected and autonomous vehicles (CAVs), most existing methods focus on model-based strategies. They require explicit knowledge of car-following dynamics of human-driven vehicles that are non-trivial to identify accurately. In this paper, instead of relying on a parametric car-following model, we introduce a data-driven non-parametric strategy, called DeeP-LCC (Data-EnablEd Predictive Leading Cruise Control), to achieve safe and optimal control of CAVs in mixed traffic. We first utilize Willems' fundamental lemma to obtain a data-centric representation of mixed traffic behavior. This is justified by rigorous analysis on controllability and observability properties of mixed traffic. We then employ a receding horizon strategy to solve a finite-horizon optimal control problem at each time step, in which input/output constraints are incorporated for collision-free guarantees. Numerical experiments validate the performance of DeeP-LCC compared to a standard predictive controller that requires an accurate model. Extensive nonlinear traffic simulations further confirm its great potential on improving traffic efficiency, driving safety, and fuel economy.

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Section: B Contributionsmentioning

confidence: 83%

Section: A Model-based and Model-free Control Of Cavsmentioning

confidence: 99%

Section: Linearized State-space Model Of Mixed Traffic Systemmentioning

confidence: 99%

Section: A Performance Validation: Comparison With Standard Mpcmentioning

confidence: 99%

mentioning

confidence: 99%

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DeeP-LCC: Data-EnablEd Predictive Leading Cruise Control in Mixed Traffic Flow

Wang¹,

Zheng²,

Li³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Safe and robust data‐driven cooperative control policy for mixed vehicle platoons

Lan

Zhao

2022

Intl J Robust & Nonlinear

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This article considers mixed platoons consisting of both human-driven vehicles (HVs) and automated vehicles (AVs). The uncertainties and randomness in human driving behaviors highly affect the platoon safety and stability. However, most existing control strategies are either for platoons of pure AVs, or for special formations of mixed platoons with known HV models. This article addresses the control of mixed platoons with more general formations and unknown HV models. An innovative data-driven policy learning strategy is proposed to design the controllers for AVs based on vehicle-to-vehicle (V2V) communications. The policy learning strategy is embedded with the constraints of control input, inter-vehicular distance error and V2V communication topology. The strategy establishes a safe and robustly stable mixed platoon using prescribed communication topologies. The design efficacy is verified through simulations of a mixed platoon with different communication topologies and leader velocity profiles.

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Improved bald eagle search algorithm with dimension learning-based hunting for autonomous vehicle including vision dynamics

Elsisi

Essa²

2022

Appl Intell

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Data-Driven Robust Predictive Control for Mixed Vehicle Platoons Using Noisy Measurement

Cited by 37 publications

References 39 publications

DeeP-LCC: Data-EnablEd Predictive Leading Cruise Control in Mixed Traffic Flow

DeeP-LCC: Data-EnablEd Predictive Leading Cruise Control in Mixed Traffic Flow

Safe and robust data‐driven cooperative control policy for mixed vehicle platoons

Improved bald eagle search algorithm with dimension learning-based hunting for autonomous vehicle including vision dynamics

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