Model Predictive Multi-Objective Vehicular Adaptive Cruise Control

Li, Shengbo; Li, Keqiang; Rajamani, Rajesh; Wang, Jianqiang

doi:10.1109/tcst.2010.2049203

Cited by 545 publications

(254 citation statements)

References 13 publications

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“…The MPC framework is widely used to tackle adaptive cruise control problems by using its capability of handling multiple constraints in a receding horizon fashion (Bageshwar et al, 2004;Mayne et al, 2000;Corona et al, 2006;Stanger and del Re, 2013;Li et al, 2011;Naus et al, 2010). A benchmark setup is proposed in (Corona and Schutter, 2008) to assess different model predictive control methods used for ACC.…”

Section: Related Workmentioning

confidence: 99%

“…The optimization problem is solved subject to desired inter-vehicle distance and acceleration limitation, which are incorporated as constraints. In (Li et al, 2011), an optimal control law is applied in order to increase tracking capabilities and fuel economy. In order to keep a safe distance between vehicles, the authors use a constant time headway spacing policy.…”

Section: Related Workmentioning

confidence: 99%

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Adaptive Cruise Control with Safety Guarantees for Autonomous Vehicles

Magdici

Althoff

2017

IFAC-PapersOnLine

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This paper addresses the problem of following a vehicle with varying acceleration in a comfortable and safe manner. Our architecture consists of a nominal controller (here: model predictive control) and a safety controller. Although model predictive control attempts to keep a safe distance, it cannot formally guarantee it, due to the assumptions on the behavior of the leading vehicle. We address this problem by holding a formally verified safety controller available. Our novel mechanism gradually engages the safety maneuver since most critical situations resolve quickly. The overall approach is evaluated against real traffic data. The results show good position and velocity tracking performance, while safety and comfort are guaranteed.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Adaptive Cruise Control with Safety Guarantees for Autonomous Vehicles

Magdici

Althoff

2017

IFAC-PapersOnLine

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“…Unlike the basic DTC or FOC with SVM, the MP-DTC strategy is based on the optimal control approach. Having the cost function designed to minimize the torque and flux control errors [32], optimized switching states can be generated.…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicle Longitudinal Stability Control Based on a New Multimachine Nonlinear Model Predictive Direct Torque Control

Sekour

Hartani

Merah

2017

Journal of Advanced Transportation

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In order to improve the driving performance and the stability of electric vehicles (EVs), a new multimachine robust control, which realizes the acceleration slip regulation (ASR) and antilock braking system (ABS) functions, based on nonlinear model predictive (NMP) direct torque control (DTC), is proposed for four permanent magnet synchronous in-wheel motors. The in-wheel motor provides more possibilities of wheel control. One of its advantages is that it has low response time and almost instantaneous torque generation. Moreover, it can be independently controlled, enhancing the limits of vehicular control. For an EV equipped with four in-wheel electric motors, an advanced control may be envisaged. Taking advantage of the fast and accurate torque of in-wheel electric motors which is directly transmitted to the wheels, a new approach for longitudinal control realized by ASR and ABS is presented in this paper. In order to achieve a high-performance torque control for EVs, the NMP-DTC strategy is proposed. It uses the fuzzy logic control technique that determines online the accurate values of the weighting factors and generates the optimal switching states that optimize the EV drives' decision. The simulation results built in Matlab/Simulink indicate that the EV can achieve high-performance vehicle longitudinal stability control.

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“…In the meanwhile a nonlinear range policy for ACC vehicles has been presented in [11] to improve traffic flow stability. Model predictive control technique has been applied to calculate the control law of ACC system in [12,13]. As an extended ACC, cooperative ACC with wireless communication has been examined for the effect on traffic flow in [14].…”

Section: Introductionmentioning

confidence: 99%

Design of Autonomous Cruise Controller with Linear Time Varying Model

Chang¹,

Yoon²,

Lee³

et al. 2015

Journal of Electrical Engineering and Technology

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-Cruise control is a technology for automatically maintaining a steady speed of vehicle as set by the driver via controlling throttle valve and brake of vehicle. In this paper we investigate cruise controller design method with consideration for distance to vehicle ahead. We employ linear time varying (LTV) model to describe longitudinal vehicle dynamic motion. With this LTV system we approximately model the nonlinear dynamics of vehicle speed by frequent update of the system parameters. In addition we reformulate the LTV system by transforming distance to leading vehicle into variation of system parameters of the model. Note that in conventional control problem formulation this distance is considered as disturbance which should be rejected. Consequently a controller can be designed by pole placement at each instance of parameter update, based on the linear model with the present system parameters. The validity of this design method is examined by simulation study.

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Model Predictive Multi-Objective Vehicular Adaptive Cruise Control

Cited by 545 publications

References 13 publications

Adaptive Cruise Control with Safety Guarantees for Autonomous Vehicles

Adaptive Cruise Control with Safety Guarantees for Autonomous Vehicles

Electric Vehicle Longitudinal Stability Control Based on a New Multimachine Nonlinear Model Predictive Direct Torque Control

Design of Autonomous Cruise Controller with Linear Time Varying Model

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