Automatic Cruise Control of a Mechatronically Steered Vehicle Convoy

Fritz, Axel; Schiehlen, Werner

doi:10.1076/vesd.32.4.331.2077

Cited by 16 publications

(3 citation statements)

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“…After the switching between engine torque output and brake control, if the system is switched to the brake control, the desired braking pressure needs to be calculated according to the desired acceleration [38], [39]. During this period, the inverse braking system model needs to be established.…”

Section: Inverse Vehicle Longitudinal Dynamicmentioning

confidence: 99%

Neural Network Sliding Mode Control of Intelligent Vehicle Longitudinal Dynamics

et al. 2019

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Longitudinal dynamics control is the basis for autonomous driving of intelligent vehicles, which have great significance to the development of intelligent transportation system (ITS). To solve the problems of traditional sliding mode control method when applied to intelligent vehicle longitudinal dynamics, such as large velocity tracking errors, strong chattering phenomenon and so on, a new sliding mode control strategy based on RBF (Radical Basis Function) neural network is presented in this paper. Firstly, a nonlinear mathematical model of the intelligent vehicle longitudinal motion is established by considering the dynamics of the engine, the torque converter, the automatic transmission and the brake system. On the basis of the system model, a variable structure control system with sliding mode is introduced to design a sliding mode variable controller with RBF neural network. This controller can adaptively adjust the switching gain and its stability is proved based on the Lyapunov theory. Finally, the effectiveness of the designed longitudinal velocity control strategy is verified by simulation under typical driving conditions. The simulation results show that the improved control algorithm can effectively suppress chattering, obtain the higher precision and stronger robustness than the traditional sliding mode control. Thus, the longitudinal motion control performance of intelligent vehicles is improved effectively. INDEX TERMS Intelligent vehicles, intelligent transportation system, longitudinal dynamics control, sliding mode control, RBF neural network.

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Section: Inverse Vehicle Longitudinal Dynamicmentioning

confidence: 99%

Neural Network Sliding Mode Control of Intelligent Vehicle Longitudinal Dynamics

et al. 2019

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“…The collision warning-collision avoidance system uses modern sensing technologies (radar, ultrasonic wave, etc.) to detect obstacles and obtain road and vehicle information (such as the speed and spacing of the leading vehicle) and then automatically warn or control vehicle braking in case of emergency (2). However, it has been criticized because of its sensor measurement errors.…”

Section: Connected Vehicle Technology Exchanges Real-time Vehicle and Traffic Information Through Vehicle-to-vehicle And Vehicle-to-inframentioning

confidence: 99%

Novel Cooperative Collision Avoidance Model for Connected Vehicles

Wang

Deng

et al. 2017

Transportation Research Record

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Connected vehicle technology exchanges real-time vehicle and traffic information through vehicle-to-vehicle and vehicle-to-infrastructure communication. The technology has the potential to improve traffic safety applications such as collision avoidance. In this paper, a novel cooperative collision avoidance (CCA) model that could improve the effectiveness of the collision avoidance system of connected vehicles was developed. Unlike traditional collision avoidance models, which relied mainly on emergency braking, the proposed CCA approach avoided collision through a combination of following vehicle deceleration and leading vehicle acceleration. Through spacing policy theory and nonlinear optimization, the model calculated the desired deceleration rate for the following vehicle and the acceleration rate for the leading vehicle, respectively, at each time interval. The CCA approach was then tested on a scaled platform with hardware-in-the-loop simulation embedded with MATLAB/Simulink and a car simulator package, CarSim. Results show that the proposed model can effectively avoid rear-end collisions in a three-vehicle platoon.

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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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Automatic Cruise Control of a Mechatronically Steered Vehicle Convoy

Cited by 16 publications

References 0 publications

Neural Network Sliding Mode Control of Intelligent Vehicle Longitudinal Dynamics

Neural Network Sliding Mode Control of Intelligent Vehicle Longitudinal Dynamics

Novel Cooperative Collision Avoidance Model for Connected Vehicles

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

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