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CLC number: TP39

On-line Access: 2017-10-25

Received: 2016-07-20

Revision Accepted: 2017-01-23

Crosschecked: 2017-09-06

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Citations:  Bibtex RefMan EndNote GB/T7714


Ming-hui Sun


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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.9 P.1385-1395


Using improved particle swarm optimization to tune PID controllers in cooperative collision avoidance systems

Author(s):  Xing-chen Wu, Gui-he Qin, Ming-hui Sun, He Yu, Qian-yi Xu

Affiliation(s):  College of Computer Science and Technology, Jilin University, Changchun 130012, China; more

Corresponding email(s):   511518984@qq.com

Key Words:  Cooperative collision avoidance system (CCAS), Improved particle swarm optimization (PSO), PID controller, Vehicle comfort, Fuel economy

Xing-chen Wu, Gui-he Qin, Ming-hui Sun, He Yu, Qian-yi Xu. Using improved particle swarm optimization to tune PID controllers in cooperative collision avoidance systems[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(9): 1385-1395.

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publisher="Zhejiang University Press & Springer",

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%T Using improved particle swarm optimization to tune PID controllers in cooperative collision avoidance systems
%A Xing-chen Wu
%A Gui-he Qin
%A Ming-hui Sun
%A He Yu
%A Qian-yi Xu
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%I Zhejiang University Press & Springer
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T1 - Using improved particle swarm optimization to tune PID controllers in cooperative collision avoidance systems
A1 - Xing-chen Wu
A1 - Gui-he Qin
A1 - Ming-hui Sun
A1 - He Yu
A1 - Qian-yi Xu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1601427

The introduction of proportional-integral-derivative (PID) controllers into cooperative collision avoidance systems (CCASs) has been hindered by difficulties in their optimization and by a lack of study of their effects on vehicle driving stability, comfort, and fuel economy. In this paper, we propose a method to optimize PID controllers using an improved particle swarm optimization (PSO) algorithm, and to better manipulate cooperative collision avoidance with other vehicles. First, we use PRESCAN and MATLAB/Simulink to conduct a united simulation, which constructs a CCAS composed of a PID controller, maneuver strategy judging modules, and a path planning module. Then we apply the improved PSO algorithm to optimize the PID controller based on the dynamic vehicle data obtained. Finally, we perform a simulation test of performance before and after the optimization of the PID controller, in which vehicles equipped with a CCAS undertake deceleration driving and steering under the two states of low speed (≤50 km/h) and high speed (≥100 km/h) cruising. The results show that the PID controller optimized using the proposed method can achieve not only the basic functions of a CCAS, but also improvements in vehicle dynamic stability, riding comfort, and fuel economy.


概要:为解决将PID控制器引入协同碰撞避免(cooperative collision avoidance system, CCAS)的研究中存在的不能合理优化PID控制器,以及对车辆行驶稳定性、舒适性及燃油经济性研究不足的问题,本文提出使用改进的粒子群优化算法(particle swarm optimization, PSO)优化PID控制器的方法,来实现CCAS对车辆更好的操控的目标。首先,本文使用PRESCAN和MATLAB/Simulink进行联合仿真,构建了由PID控制器,机动策略判断模块组成的CCAS。其次,本文使用改进的粒子群算法,依据获得的汽车动力学数据,对PID控制器进行了优化。最后,本文模拟了配备CCAS的车辆在其PID控制器经过优化前后,在低速(≤50 km/h)和高速(≥100 km/h)两种巡航状态下,进行减速行驶、减速转向工况的测试。结果表明,经过本文方法优化的PID控制器,不仅可使CCAS实现基本功能,还可实现车辆动态稳定性,行驶舒适性和燃油经济性的改善。


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[1]CAN Newsletter Online, 2014. Emergency Steering Assist. http://can-newsletter.org/engineering/engineering-miscellaneous/140813_emergency-steering-assist

[2]Chen, L.W., Chou, P.C., 2013. A lane-level cooperative collision avoidance system based on vehicular sensor networks. 19th Annual Int. Conf. on Mobile Computing & Networking, p.131-134.

[3]Cho, H., Kim, B., 2014. Cooperative intersection collision-warning system based on vehicle-to-vehicle communication. Contemp. Eng. Sci., 7(22):1147-1154.

[4]Feng, Y.Q., Ji, H.Q., Liu, Z.S., 2012. Parameter setting and simulation of the PID controller for vehicle spacing control system. China Sci. Technol. Inform., 2012(8):141-143 (in Chinese).

[5]Huang, C.M., Lin, S.Y., 2014. Cooperative vehicle collision warning system using the vector-based approach with dedicated short range communication data transmission. IET Intell. Transp. Syst., 8(2):124-134.

[6]Jin, C., Wang, J., Ma, J., et al., 2010. Application of improved PSO for parameter tuning of PID controller. J. Electron. Meas. Instrum., 24(2):141-146.

[7]Khan, H., Iqbal, J., Baizid, K., et al., 2015. Longitudinal and lateral slip control of autonomous wheeled mobile robot for trajectory tracking. Front. Inform. Technol. Electron. Eng., 16(2):166-172.

[8]Kim, K.I., Guan, H., Wang, B., et al., 2016. Active steering control strategy for articulated vehicles. Front. Inform. Technol. Electron. Eng., 17(6):576-586.

[9]Konstantinidis, E.I., Patoulidis, G.I., Vandikas, I.N., et al., 2010. Development of a collaborative vehicle collision avoidance system. IEEE Intelligent Vehicles Symp., p.1066-1071.

[10]Kreuzen, C., 2012. Cooperative Adaptive cruise control using information from multiple predecessors in combination with MPC. MS Thesis, Delft University of Technology, Delft, the Netherlands.

[11]Lee, D.H., Bai, S.N., Kim, T.W., et al., 2010. Enhanced selective forwarding scheme for alert message propagation in VANETs. Int. Conf. on Information Science and Applications, p.1-9.

[12]Mirfakhraie, T., He, Y., Liscano, R., 2014. Wireless networked control for active trailer steering systems of articulated vehicles. ASME Int. Mechanical Engineering Congress and Exposition, Volume 12: Transportation Systems, p.V012T15A003.

[13]Ong, H.Y., Gerdes, J.C., 2015. Cooperative collision avoidance via proximal message passing. American Control Conf., p.4124-4130.

[14]Seo, H.S., Jung, J.S., Lee, S.S., 2014. Network performance analysis and manuever model for overtaking assistant service using wave. Int. J. Autom. Technol., 15(1):57-64.

[15]Shi, Y., Eberhart, R., 1998. A modified particle swarm optimizer. IEEE Int. Conf. on Evolutionary Computation, p.69-73.

[16]Shi, Y., Eberhart, R.C., 2001. Fuzzy adaptive particle swarm optimization. Congress on Evolutionary Computation, p.101-106.

[17]Solyom, S., Bengtsson, M., 2012. Collision Avoidance System in a Vehicle. US Patent 8 200 420.

[18]Tan, H.S., Huang, J., 2006. DGPS-based vehicle-to-vehicle cooperative collision warning: engineering feasibility viewpoints. IEEE Trans. Intell. Transp. Syst., 7(4): 415-428.

[19]Wang, Q., Phillips, C., 2013. Cooperative collision avoidance for multi-vehicle systems using reinforcement learning. 18th Int. Conf. on Methods & Models in Automation & Robotics, p.98-102.

[20]Wang, Q., Zhu, S., He, Y., 2015. Model reference adaptive control for active trailer steering of articulated heavy vehicles. SAE Technical Papers, 2015-01-1495.

[21]Wang, Q.G., Zou, B., Lee, T.H., et al., 1997. Auto-tuning of multivariable PID controllers from decentralized relay feedback. Automatica, 33(3):319-330.

[22]Wu, Y.H., Lu, Y.P., 2009. Main factors and evaluation methods of driving comfort. Heilongjiang Jiaotong Keji, 2009(8): 197-198 (in Chinese).

[23]Yan, G., Yang, W., Weigle, M.C., et al., 2010. Cooperative collision warning through mobility and probability prediction. IEEE Intelligent Vehicles Symp., p.1172-1177.

[24]Yu, C.B., Wang, Y.Q., Shao, J.L., 2016. Optimization of formation for multi-agent systems based on LQR. Front. Inform. Technol. Electron. Eng., 17(2):96-109.

[25]Zardosht, B., Beauchemin, S., Bauer, M.A., 2013. A decision making module for cooperative collision warning systems using vehicular ad-hoc networks. 16th Int. IEEE Conf. on Intelligent Transportation Systems, p.1743-1749.

[26]Zhang, H.T., Hu, H.L., Wang, B., 2008. A modified PSO algorithm and its application in tuning of PID. Techn. Autom. Appl., 27(12):14-16.

[27]Zhang, J.M., Li, Q., Cheng N., et al., 2013. Nonlinear path-following method for fixed-wing unmanned aerial vehicles. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 14(2):125-132.

[28]Zhang, M.H., Duan, D.P., Chen, L., 2012. Turning mechanism and composite control of stratospheric airships. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 13(11): 859-865.

[29]Zhu, X., Liu, Z., Li, L., 2015. Evasive manoeuvre for emergency steering based on typical vehicle-pedestrian use case. J. Autom. Safety Energy, 6(3):217-223 (in Chinese).

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