Fuzzy Longitudinal Controller Design and Experimentation for Adaptive Cruise Control and Stop&amp;Go

Tsai, Ching‐Chih; Hsieh, Shih-Min; Chen, Chien-Tzu

doi:10.1007/s10846-010-9393-z

Cited by 42 publications

(23 citation statements)

References 19 publications

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“…According to these published literatures, most scholars used fuzzy control [10], [29], model predictive control [11], [24], and robust control [12] to regulate the longitudinal velocity of the vehicles and improve the accuracy of speed tracking. However, the disadvantage of these control methods is that they require specific models and complex structures.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

Neural Network Sliding Mode Control of Intelligent Vehicle Longitudinal Dynamics

et al. 2019

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“…It is worth mentioning that Stop & Go systems are applied to deal with the city traffic condition when the velocity is less than 40 km/h. [8][9][10] It can realize vehicle's stationary control at low velocity and follow actively the preceding car. So, the system is better for the city traffic environment, especially the traffic congestion or the stop-go condition.…”

Section: Introductionmentioning

confidence: 99%

A synergetic strategy of automobile intelligent cruise system based on fuzzy control adopting hierarchical structure

Chen

Zhou

Zhang

et al. 2019

International Journal of Advanced Robotic Systems

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To improve automobile travel safety and relieve the intension of driving fatigue, a synergetic strategy on the throttle and brake combined control of automobile cruise system is proposed based on fuzzy control theory. A hierarchical control structure, including the upper fuzzy controller and the lower model match controller, is designed for integrated control function. The upper controller consists of the two input variables, that is, the deviation of the theoretical safe distance and the relative distance and the relative velocity between the host car and the preceding vehicle, and the single output variable, that is, the excepted acceleration of host car. The lower controller has achieved the fast compensation of excepted acceleration by adopting parallel feedforward and feedback compensator to input into the inverse dynamics model of automobile cruise system. Using MATLAB/Simulink, the control function on the proposed synergetic strategy is validated for different working cases. The results show that the synergetic strategy possesses an integrated capability involved automated stop & go control, and following the preceding car to prevent rear-end collision, and the constant velocity cruise whether the driving conditions of the city road or expressway.

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“…Tsai et al 1 have presented a fuzzy longitudinal control system with car-following speed ranging from 0 to 120 km/h to realize the main functions of both ACC and stop & go control. Sivaji and Sailaja 2 have proposed ACC for stop & go maneuvers of an intelligent vehicle using hybrid proportional-integral-derivative (PID) controller.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy control for vehicle status estimation considering roll stability and its application in target recognition of automobile cruise system

Chen

Zhang

Yue-Zhou

et al. 2017

Advances in Mechanical Engineering

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In order to resolve the goal confusion or missing on curve road, a target recognition method on automobile cruise system is put forward based on filtering and fuzzy control theory. A vehicle model with 3 degrees of freedom on the yaw rate, sideslip angle, and body roll angle is discretized and an extended Kalman filter is proposed for estimating the road curvature. For the key target on the straight road, a multi-object changing-lane fuzzy controller is presented to prejudge the dangerous changing-lane objects. For the recognition of key target on the curve road, a recognition model is set up to calculate the lateral offset between the estimated road curvature radius and the target trajectory radius. In order to validate the feasibility of the key target recognition method, the simulation cases are given for multi-objects on the curve road and straight road adopting Carsim and MATLAB/Simulink co-simulation. The simulation results show that designed fuzzy controller has good robustness for prejudging the dangerous changing-lane objects on straight road. The extended Kalman filter has realized the efficient estimation on road curvature and the recognition model has identified the key target accurately to prevent the goal confusion or missing on curve road. The research results can offer the theory reference for improving or perfecting the control performances of automobile intelligent cruise system.

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Fuzzy Longitudinal Controller Design and Experimentation for Adaptive Cruise Control and Stop&Go

Cited by 42 publications

References 19 publications

Neural Network Sliding Mode Control of Intelligent Vehicle Longitudinal Dynamics

Neural Network Sliding Mode Control of Intelligent Vehicle Longitudinal Dynamics

A synergetic strategy of automobile intelligent cruise system based on fuzzy control adopting hierarchical structure

Fuzzy control for vehicle status estimation considering roll stability and its application in target recognition of automobile cruise system

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