Nonlinear ACC in Simulation and Measurement

Fritz, Axel; Schiehlen, Werner

doi:10.1076/vesd.36.2.159.3556

Cited by 12 publications

(5 citation statements)

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“…In literature [93], a multiobjective collaborative ACC system was designed based on model predictive control, which solved the dilemma that the traditional ACC system was only suitable for simple distance keeping in a single working condition, and further optimized the problems of high computational amount and nonfeasible solution of model predictive control. Fritz and Schiehlen, based on the sliding mode control algorithm, solved the influence of external disturbance on the instability of low-speed following and at the same time optimized the tracking of ACC system to improve the robustness of the system [94]. Naranjo et al also designed the lower controller of the adaptive cruise system based on fuzzy control to realize the control of engine throttle opening and brake pedal opening [95,96].…”

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confidence: 99%

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

et al. 2021

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Environmental perception technology is the basis and premise of intelligent vehicle decision control of intelligent vehicles, a crucial link of intelligent vehicles to realize intelligence, and also the basic guarantee of its safety and intelligence. The accuracy and robustness of the perception algorithm will directly affect or even determine the realization of the upper function of intelligent vehicles. The wrong environmental perception will affect the control of the vehicle, thus causing safety risks. This paper discusses the intelligent vehicle perception technology and introduces the development status and control strategies of several important sensors such as machine vision, laser radar, and millimeter-wave radar. Target detection, target recognition, and multisensor fusion are analyzed in the optimized part of sensor results. The functions of the intelligent vehicle assistance system which has been applied to the ground at present are described, and the lane detection, adaptive cruise control (ACC), and autonomous emergency braking (AEB) are analyzed. Finally, the paper looks forward to the research direction of sense-based intelligent vehicle perception technology, which will play an important role in guiding the development of intelligent vehicles and accelerate the landing process of intelligent vehicles.

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confidence: 99%

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

et al. 2021

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“…To fulfill the longitudinal control of the follower vehicle, fuzzy logic approaches are proposed in the studies by Muller and Nocker 17 and Chang and Choi. 18 Slidingmodel and fuzzy sliding-model controllers are presented in the studies by Fritz and Schiehlen 19 , Xiao and Gao 20 , and Ghaffari et al 21 In the studies by Moon et al 22 and Kim et al, 23 a linear optimal control theory-based upper-level controller which outputs the appropriate acceleration is proposed for the full-range adaptive cruise. And a model free low-level controller is utilized to regulate throttle and brake input to follow the desired acceleration.…”

Section: Introductionmentioning

confidence: 99%

An adaptive longitudinal control method for autonomous follow driving based on neural dynamic programming and internal model structure

Zhu

Dai

Huang

et al. 2017

International Journal of Advanced Robotic Systems

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Autonomous vehicles are considered to have great potentials in improving transportation safety and efficiency. Autonomous follow driving is one of the highly probable application forms of autonomous vehicles in the near future. In this article, we aim at the basic autonomous following form with one follower and one leader. Proper longitudinal regulation of the follower vehicle is essential for the driving quality of the two-vehicle platoon. Focusing on this problem, a novel longitudinal control method composing of a learning-based acceleration decision phase and an internal model-based acceleration tracking phase is proposed for the follower vehicle. In the acceleration decision phase, proper acceleration commands of the follower that adjusts the following distance converging to the target value are determined by a nearoptimal acceleration policy which is obtained through an online reinforcement learning algorithm named neural dynamic programming. In the acceleration tracking phase, throttle and brake control commands that drive the vehicle as the decided acceleration are derived by an internal model control structure. The performance of our proposed method is verified by simulation experiments conducted with CarSim, an industry recognized vehicle dynamic simulator.

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“…The majority to handle non-linearities are to linearize powertrain dynamics, including exact linearization [15,16], Taylor linearization [17] and inverse model compensation [12,18]. Fritz and Schiehlen [15,16] use the exact linearization technique to normalize node dynamics for synthesis of cruising control. After linearization, a pole placement controller was employed to control the exactly linearized states.…”

Section: Introductionmentioning

confidence: 99%

Robust Accelerating Control for Consistent Node Dynamics in a Platoon of CAVs

Gao¹,

Li²,

Li³

2016

Autonomous Vehicle

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Driving as a platoon has potential to significantly benefit traffic capacity and safety. To generate more identical dynamics of nodes for a platoon of automated connected vehicles (CAVs), this chapter presents a robust acceleration controller using a multiple model control structure. The large uncertainties of node dynamics are divided into small ones using multiple uncertain models, and accordingly multiple robust controllers are designed. According to the errors between current node and multiple models, a scheduling logic is proposed, which automatically selects the most appropriate candidate controller into loop. Even under relatively large plant uncertainties, this method can offer consistent and approximately linear dynamics, which simplifies the synthesis of upper level platoon controller. This method is validated by comparative simulations with a sliding model controller and a fixed H ∞ controller.

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Nonlinear ACC in Simulation and Measurement

Cited by 12 publications

References 0 publications

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

[Retracted] Sensor‐Based Environmental Perception Technology for Intelligent Vehicles

An adaptive longitudinal control method for autonomous follow driving based on neural dynamic programming and internal model structure

Robust Accelerating Control for Consistent Node Dynamics in a Platoon of CAVs

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