Distributed Model Predictive Control for Cooperative and Flexible Vehicle Platooning

Liu, Peng; Kurt, Arda; Özgüner, Ü.

doi:10.1109/tcst.2018.2808911

Cited by 75 publications

(28 citation statements)

References 35 publications

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“…The equality holds due to the definitions ofx a i (k|t + 1) and u a i (k|t + 1) in (19) and (20), respectively. Subtracting the equation (23) from (24) yields…”

Section: With the Terminal Stateξmentioning

confidence: 99%

See 1 more Smart Citation

Unknown Input Observer Based Approach for Distributed Tube-Based Model Predictive Control of Heterogeneous Vehicle Platoons

Luo

Nguyen

Fleming

et al. 2021

IEEE Trans. Veh. Technol.

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This paper addresses the control problem of heterogeneous vehicle platoons subject to disturbances and modeling errors. The objective is to guarantee spatialgeometry constraints of vehicles in a platoon. We deal with the case where a predecessor-leader following (PLF) communication topology is used and heterogeneous vehicle dynamics is subject to disturbances. To estimate the lumped disturbance, the technique of unknown input proportional multiple-integral (PMI) observer is employed such that both the state and the disturbance are simultaneously estimated. Moreover, tube-based model predictive control (TMPC) is used and the corresponding control law is composed of a feed-forward term, a feedback term, and a disturbance compensation term. The gains in the integrated control strategy are optimized by utilizing the particle swarm optimization (PSO) algorithm with an H∞ performance index of an augmented error system. It is proved that the deviations between the actual system and the nominal system are bounded in a robustly positively invariant (RPI) set, that is, the main objective is guaranteed. With the proposed control strategy, simulations and comparisons are carried out. We can see that the control performance of the proposed strategy is significantly improved while the computational time is reduced compared with existing methods. Index Terms-Vehicle platoon, integrated controller, tube-based model predictive control (TMPC), proportional multiple-integral (PMI) observer.

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“…The equality holds due to the definitions ofx a i (k|t + 1) and u a i (k|t + 1) in (19) and (20), respectively. Subtracting the equation (23) from (24) yields…”

Section: With the Terminal Stateξmentioning

confidence: 99%

“…An H ∞ control method was employed in [22], [23]. Moreover, distributed model predictive control (DMPC) has been applied in various contexts [16], [24], [25]. From the application perspective, the platoon control is a multi-objective optimization problem under multiple physical constraints and external disturbances.…”

Section: Introductionmentioning

confidence: 99%

Unknown Input Observer Based Approach for Distributed Tube-Based Model Predictive Control of Heterogeneous Vehicle Platoons

Luo

Nguyen

Fleming

et al. 2021

IEEE Trans. Veh. Technol.

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show abstract

“…Recent developments in autonomous vehicles have seen the coordinated control of MAS being applied to unmanned aerial vehicles, robots and aircraft [21]. Current techniques for the coordinated control of MAS include dynamic programming [22], fuzzy-based model [23], model predictive control [24]- [26], machine learning [27]- [29].…”

Section: Coordinated Control Of Masmentioning

confidence: 99%

Adaptive Regret Minimization for Learning Complex Team-Based Tactics

et al. 2019

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This paper presents an approach and analysis for performing decentralized cooperative control of a team of decoys to achieve the Honeypot Ambush tactic. In this tactic, the threats are successfully lured into a designated region where they can be easily defeated. The decoys learn to cooperate by incorporating a game-theory-based online-learning method, known as regret minimization, to maximize the team's global reward. The decoy agents are assumed to have physical limitations and to be subject to certain stringent range constraints required for deceiving the networked threats. By employing an efficient coordination mechanism, the agents learn to be less greedy and allow weaker agents to catch up on their rewards to improve team performance. Such a coordination solution corresponds to achieving convergence to coarse correlated equilibrium. The numerical results verify the effectiveness of the proposed solution to achieve a global satisfaction outcome and to adapt to a wide spectrum of scenarios.

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“…Then, the optimal prediction sequence is exchanged by V2V communication. Finally, the first term of the optimal prediction sequence is selected as the input to each distributed controller [16]. In the DMPC method, each distributed controller solves an optimal control problem within a finite horizon and exchanges optimization results through V2V communication.…”

Section: Introductionmentioning

confidence: 99%

“…In the DMPC method, each distributed controller solves an optimal control problem within a finite horizon and exchanges optimization results through V2V communication. Then, the first term in each optimized sequence is applied to the corresponding distributed controller [16]. Recently, several DMPC methods have been applied for vehicle platooning.…”

Section: Introductionmentioning

confidence: 99%

Integrated Communication and Control Design for Fuel-Efficient Vehicle Platooning

Wen

2021

Electronics

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As a promising application for autonomous driving, vehicle platooning aims at increasing traffic throughput, improving road safety, and reducing air pollution and fuel consumption. However, frequent traffic perturbations will bring more fuel consumption because vehicles driving in a platoon require more control to ensure safe driving, especially in high-density scenes. In this paper, considering the traffic perturbations and high-density scenes, we integrate communication and control systems to reduce the fuel consumption of a platoon. By obtaining the velocities of multiple vehicles ahead through a long-term evolution-vehicle (LTE-V) network, we propose a modified distributed model predictive control (DMPC) method to smooth traffic perturbations and handle the constraints of vehicle state and control. In addition, considering a limited number of uplink channels that can be reused in the platoon and the uncertainty of wireless channels, a radio resource allocation optimization problem in the LTE-V network is modeled. This problem is solved in two steps including maximum vehicle-to-vehicle (V2V) broadcast distance and minimum weight matching. This resource allocation scheme increases the platoon-based V2V broadcast distance while ensuring the ergodic capacity requirement of the cellular user (CUE) uplink communication and the reliability of platoon-based V2V communication. Simulation results show that the proposed method improves fuel efficiency compared to the existing schemes.

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Distributed Model Predictive Control for Cooperative and Flexible Vehicle Platooning

Cited by 75 publications

References 35 publications

Unknown Input Observer Based Approach for Distributed Tube-Based Model Predictive Control of Heterogeneous Vehicle Platoons

Unknown Input Observer Based Approach for Distributed Tube-Based Model Predictive Control of Heterogeneous Vehicle Platoons

Adaptive Regret Minimization for Learning Complex Team-Based Tactics

Integrated Communication and Control Design for Fuel-Efficient Vehicle Platooning

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