合肥工业大学陈无畏教授团队讲述基于Takagi-Sugeno模糊模型/H∞控制的车道保持系统并行分布式补偿 | CJME论文推荐

Original 陈无畏，赵林峰等机械工程学报 2022-04-23

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引用本文

Wuwei Chen, Linfeng Zhao, Huiran Wang, et al. Parallel Distributed Compensation/H∞ Control of Lane-keeping System Based on the Takagi-Sugeno Fuzzy Model. Chinese Journal of Mechanical Engineering, 2020, 33: 61.

研究背景及目的

Lane keeping is an important function for vehicles equipped with advanced driver assistance systems. The lane keeping system to assist the vehicle to travel along the lane centerline based on the vehicle-road information. The tracking performance of the vehicle for the lane centerline determines the performance of the lane keeping system. In order to further improve the performance of the lane keeping system, it is necessary to conduct further research to reduce the tracking error of the vehicle.

试验方法

In order to more accurately evaluate the reliability of the lane keeping assist system, real vehicle test is carried out. When the vehicle is driving on the road, the vehicle camera mounted on the rear view mirror obtains forward road image, which is sent to the digital signal processor (DSP) for lane detection and identification so as to determine lane information including lane shape, position and direction of the vehicle information, etc.

The vehicle position and direction deviation related to lane line are obtained through the transformation of actual road environment and image coordinates.DSP sends the information in the form of CAN to CompactRIO device interface system. Via speed, steering wheel angle and torque sensors, the system gets real-time vehicle speed, steering wheel torque measurement and motor rotation signal, and sends these signals to the CompactRIO interface system. The CompactRIO interface system calculates the assist torque vehicle needed according to the location information and the vehicle state information from vehicle sensors. System sends the expected assist torque signal and the feedforward compensation control signal to the motor angle controller independently researched and developed. Then motor controller drive motor tracks the desired angle and performing active steering correction, finally realizing lane keeping.

结果

The control method of H∞ control based on T-S model, can ensure that both the vehicle lateral deviation and yaw angle are within a safe range on roads of both in the high road adhesion coefficient and in the low adhesion coefficient road. Vehicle driving deviation and performance index of three methods on high adhesion coefficient are compared. As Table 1 shows, compared with H∞ control, the vehicle lateral deviation is reduced by 39.81% and the performance index is increased by 28.08% under the H∞ control based T-S model. Vehicle lateral deviation is reduced by 51.52%, performance index is increased by 42.44% under composite control (H∞ control based T-S model and feed-forward control). The results show that the composite control method has smaller lateral deviation and smaller performance index, which reflects the higher accuracy of lane keeping.

结论

(1) The driver-vehicle-road closed-loop system based on T-S model with the fuzzy variable of the longitudinal velocity is established. And the controller of lane keeping is designed combining the two-point preview driver model. The experiment results show that the designed controller can keep the vehicle running close to the center lane line, and the system has good robustness and stability.

(2) The H∞ controller based on T-S model is designed for the desired assist torque, in the mean time considering external resistance affecting the wheel when steering. The feedforward compensation controller is designed to provide additional assist torque to compensate for external resistance. The simulation results show that the precision of lane keeping is higher under composite control.

(3) The lane recognition system based on machine vision and lane keeping control system are installed on the real vehicle. Image processor, decision controller and motor controller communicate through CAN. Multiple real vehicle test results are conducted to show that the vehicle experiment platform is built to achieve the function of lane keeping, and the stability of the vehicle in process is guaranteed.

前景与应用

PDC/H∞ Control can improve the tracking performance of the vehicle for the lane centerline. Lane Keeping System using PDC/H∞ Control can be applied to vehicles with advanced driver assistance systems to further improve the vehicle's driving safety.

相关文章/图书推荐

1、Tan D , Chen W , Wang H , et al. Shared control for lane departure prevention based on the safe envelope of steering wheel angle[J]. Control Engineering Practice, 2017, 64(jul.):15-26.

2、Tan D. Human-machine Sharing and Hierarchical Control Based Lane Departure Assistance System[J]. Journal of Mechanical Engineering, 2015, 51(22).

3、Wang H, Wang Q, Chen W, et al. Multi-mode human–machine cooperative control for lane departure prevention based on steering assistance and differential braking[J]. IET Intelligent Transport Systems, 2020, 14(6) 578-588.

4、Lee J , Choi J , Yi K , et al. Lane-keeping assistance control algorithm using differential braking to prevent unintended lane departures[J]. Control Engineering Practice, 2014, 23(feb.):1-13.

团队带头人介绍

Wuwei Chen

He is currently a Professor with the School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei. He has authored or coauthored more than 300 research publications. He has engaged in more than ten sponsored projects. His current research interests include vehicle dynamics and integrated control, driving-assistance systems, as well as intelligent vehicles.

团队研究方向

In recent years, the research group is mainly engaged in research work on automotive dynamics and control, active safety and integrated control of intelligent vehicles, and autonomous driving technology.The research team has obtained rich research results in modeling and simulation of automotive dynamics systems, EPS,steering by wire, electronic stability control and active braking control, integrated control of automotive chassis, integrated control of ADAS and active fault-tolerant control, visual navigation and path planning of intelligent vehicles, and has accumulated a profound theoretical basis and practical experience.

近两年团队发表文章

1、Wang H, Wang Q, Chen W, et al. Multi-mode human–machine cooperative control for lane departure prevention based on steering assistance and differential braking[J]. IET Intelligent Transport Systems, 2020, 14(6) 578-588.

2、Wang H, Cui W, Xia Z, et al. Vehicle lane keeping system based on TSK fuzzy extension control[J]. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2020, 234(2-3): 762-773.

3、Zhengang G , Wuwei C , Dongkui T , et al. Human-machine Cooperative Lane Departure Assist Control Considering Driver Manipulate Failure[J]. Journal of Mechanical Engineering, 2019, 55(16):91.

4、XIE Youhao, ZHAO Linfeng, CHEN Wuwei, LIU Yanlin. Road Feel and Return Control on Low Friction Coefficient Road for Steer-by-wire Vehicles[J]. Journal of Mechanical Engineering, 2019, 55(16): 148-158.

5、Wang Q, Wei Z, Wang J, et al. Curve recognition algorithm based on edge point curvature voting[J]. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2019: 0954407019866975.

6、Chen W, Zhao L, Tan D, et al. Human–machine shared control for lane departure assistance based on hybrid system theory[J]. Control Engineering Practice, 2019, 84: 399-407.

7、Hongbo W , Zhi X , Wuwei C . Lane Departure Assistance Control Based on Extension Combination of Steering and Braking Systems Considering Human-machine Coordination[J]. Journal of Mechanical Engineering, 2019.

8、Chen W , Wang X , Tan D . Study on the Grey Predictive Extension Control of Yaw Stability of Electric Vehicle Based on the Minimum Energy Consumption[J]. Journal of Mechanical Engineering, 2018.

9、Wang H, Cui W, Lin S, et al. Stability control of in-wheel motor drive vehicle with motor fault[J]. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2019, 233(12): 3147-3164.

10、Zhao L , Chen W , Wang J , et al. Research on Steering-by-wire Control Strategy Based on Extension Sliding Mode Control[J]. Journal of Mechanical Engineering, 2019, 55(2):126-134.

11、Wuwei Chen，Rongyun Zhang，Linfeng Zhao，et al. Control of chaos in vehicle lateral motion using the sliding mode variable structure control[J]. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2019，776-789.

12、CHEN W , TAN D , ZHAO L , et al. Vehicle Sideslip Angle and Road Friction Estimation Using Online Gradient Descent Algorithm[J]. IEEE Transactions on Vehicular Technology, 2018:11475-11485.

13、Gao Z , Chen W , Tan D . Active Fault-tolerant Control of EPS Considering Sensor and Actuator Fault[J]. Journal of Mechanical Engineering, 2018, 54(22).

14、Chen W . Study on Extension Decision and Artificial Potential Field Based Lane Departure Assistance System[J]. Journal of Mechanical Engineering, 2018, 54(16):134.

15、Chen W , Xu K , Tan D , et al. Study on hybrid control based steering system for intelligent vehicle in high-speed condition[J]. Scientia Sinica, 2018, 48(6).

16、Wang Q , Liu W , Chen W , et al. Sliding Mode Control of Vehicle Electronic Stability Program Based on Road Identification[J]. Qiche Gongcheng/Automotive Engineering, 2018, 40(1):82-90 and 106.

17、Zhao L , Shao W , Xu F , et al. EPS Friction Compensation with Adaptive Neural Network Based on Back-stepping Method[J]. Qiche Gongcheng/Automotive Engineering, 2018, 40(12):1454-1460 and 1474.

18、Wang Q D , Wang X Y , Huang H . Driver's Intention Identification Based Lane Departure Avoidance Bounded Control[J]. Zhongguo Gonglu Xuebao/china Journal of Highway & Transport, 2018, 31(3):105-115.

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