Model predictive control policy design, solutions, and stability analysis for longitudinal vehicle control considering shockwave damping

Wang, Yizhou; Jin, Peter J.

doi:10.1016/j.trc.2023.104038

Cited by 7 publications

(2 citation statements)

References 51 publications

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“…Moreover, if the vehicles can exchange information through communication networks about their velocity, acceleration, or position, then the CACC functionality can be used to ensure improved performances obtained by the ACC. The most used approaches for the longitudinal control are also based on the MPC algorithm [62,77,78], LQR [79], and PID controllers [80]. The advantage of using the MPC strategy and vehicle communication compared to the other methods consists of the possibility to use the future actions' predictions of a neighbouring vehicle.…”

Section: Literature Reviewmentioning

confidence: 99%

Control Architecture for Connected Vehicle Platoons: From Sensor Data to Controller Design Using Vehicle-to-Everything Communication

Lazar,

Pauca,

Maxim

et al. 2023

Sensors

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A suitable control architecture for connected vehicle platoons may be seen as a promising solution for today’s traffic problems, by improving road safety and traffic flow, reducing emissions and fuel consumption, and increasing driver comfort. This paper provides a comprehensive overview concerning the defining levels of a general control architecture for connected vehicle platoons, intending to illustrate the options available in terms of sensor technologies, in-vehicle networks, vehicular communication, and control solutions. Moreover, starting from the proposed control architecture, a solution that implements a Cooperative Adaptive Cruise Control (CACC) functionality for a vehicle platoon is designed. Also, two control algorithms based on the distributed model-based predictive control (DMPC) strategy and the feedback gain matrix method for the control level of the CACC functionality are proposed. The designed architecture was tested in a simulation scenario, and the obtained results show the control performances achieved using the proposed solutions suitable for the longitudinal dynamics of vehicle platoons.

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Section: Literature Reviewmentioning

confidence: 99%

Control Architecture for Connected Vehicle Platoons: From Sensor Data to Controller Design Using Vehicle-to-Everything Communication

Lazar,

Pauca,

Maxim

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Driver intention anticipation is important because only half of the drivers turn on signals before lane-change maneuvers. To predict these intentions can also reduce traffic congestion [12,13]. Although these systems are considered the lowest automation levels (Levels 1 and 2), there is evidence that they contribute to lane maintenance [14].…”

Section: Introductionmentioning

confidence: 99%

Vehicle Dynamics in Electric Cars Development Using MSC Adams and Artificial Neural Network

Cachumba-Suquillo,

Alfaro-Ponce,

Torres-Cedillo

et al. 2023

WEVJ

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Recently, there has been renewed interest in lightweight structures; however, a small structure change can strongly affect vehicle dynamic behavior. Therefore, this study provides new insights into non-parametric modeling based on artificial neural networks (ANNs). This work is then motivated by the requirement for a reliable substitute for virtual instrumentation in electric car development to enable the prediction of the current value of the vehicle slip from a given time history of the vehicle (input) and previous values of synthetic data (feedback). The training data are generated from a multi-body simulation using MSC Adams Car; the simulation involves a double lane-change maneuver. This test is commonly used to evaluate vehicle stability. Based on dynamic considerations, this study implements the nonlinear autoregressive exogenous (NARX) identification scheme used in time-series modeling. This work presents an ANN that is able to predict the side slip angle from simulated training data generated employing MSC Adams Car. This work is a specific solution to overtake maneuvers, avoiding the loss of vehicle control and increasing driving safety.

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A jam-absorption driving system for reducing multiple moving jams by estimating moving jam propagation

Li,

Yanagisawa,

Nishinari

2024

Transportation Research Part C: Emerging Technologies

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Model predictive control policy design, solutions, and stability analysis for longitudinal vehicle control considering shockwave damping

Cited by 7 publications

References 51 publications

Control Architecture for Connected Vehicle Platoons: From Sensor Data to Controller Design Using Vehicle-to-Everything Communication

Control Architecture for Connected Vehicle Platoons: From Sensor Data to Controller Design Using Vehicle-to-Everything Communication

Vehicle Dynamics in Electric Cars Development Using MSC Adams and Artificial Neural Network

A jam-absorption driving system for reducing multiple moving jams by estimating moving jam propagation

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