Abstract
In order to solve the problem that automatic train operation control system considering the single factor and control is not easy to be accurate, a multi-objective optimization (MO) based on improved genetic algorithm (GA) and fuzzy PID control method is proposed in this paper. Firstly, based on train operation characteristics, a multi-objective model of train operation process is established. Secondly, in order to improve the performance of the algorithm, the train operation process is optimized by using linear weight method and multi-objective genetic algorithm. Third, in order to suppress the local convergence of GA, a dual population genetic mechanism is adopted in the iterative process. Finally, a fuzzy PID controller is embedded into the control designer after target curve and control train operation in real time according to the real time running state. The results show that the proposed algorithm can get a reasonable MO result and accurate real-time control.
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Deb, K.: Solving goal programming problems using multi-objective genetic algorithms. In: Evolutionary Computation, CEC 1999 (1999)
Gao, S., Dong, H., Chen, Y., et al.: Approximation-based robust adaptive automatic train control: an approach for actuator saturation. IEEE Trans. Intell. Transp. Syst. 14(4), 1733–1742 (2013)
Lejeune, A., Chevrier, R., Rodriguez, J.: Improving an evolutionary multi-objective approach for optimizing railway energy consumption. Procedia - Soc. Behav. Sci. 48, 3124–3133 (2012)
DomíNguez, M., FernáNdez-Cardador, A., Cucala, A.P., et al.: Energy savings in metropolitan railway substations through regenerative energy recovery and optimal design of ATO speed profiles. IEEE Trans. Autom. Sci. Eng. 9(3), 496–504 (2012)
Sheu, J.W., Lin, W.S.: Adaptive optimal control for designing automatic train regulation for metro line. IEEE Trans. Control Syst. Technol. 20(5), 1319–1327 (2012)
Chen, D., Chen, R., Li, Y., et al.: Online learning algorithms for train automatic stop control using precise location data of balises. IEEE Trans. Intell. Transp. Syst. 14(3), 1526–1535 (2013)
Shangguan, W., Yan, X., Cai, B., et al.: Multiobjective optimization for train speed trajectory in CTCS high-speed railway with hybrid evolutionary algorithm. IEEE Trans. Intell. Transp. Syst. 16(4), 2215–2225 (2015)
Gu, Q., Lu, X.Y., Tang, T.: Energy saving for automatic train control in moving block signaling system. In: International IEEE Conference on Intelligent Transportation Systems, pp. 1305–1310. IEEE (2011)
Costa, A., Cappadonna, F.A., Fichera, S.: A hybrid genetic algorithm for minimizing makespan in a flow-shop sequence-dependent group scheduling problem. J. Intell. Manuf. 28(6), 1–15 (2017)
Liu, T.: Application of a variable-universe and self-adaptive fuzzy PID controller in DC motor speed control system. J. Chem. Technol. Biotechnol. 79(79), 486–490 (2011)
Control of the Dual-closed Loops Speed Governing System of DC Motor by Self-adaptive Fuzzy PID Controller. Mechatronics (2009)
Cui, Y.L., Lu, H.L., Fan, J.B.: Design and simulation of cascade fuzzy self - adaptive PID speed control of a thyristor-driven DC motor, pp. 655–660. IEEE (2006)
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This work is supported by Nature Science Foundation of China under Grand 60574018.
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Wang, L., Wang, X., Sun, D., Hao, H. (2017). Multi-objective Optimization Improved GA Algorithm and Fuzzy PID Control of ATO System for Train Operation. In: Yue, D., Peng, C., Du, D., Zhang, T., Zheng, M., Han, Q. (eds) Intelligent Computing, Networked Control, and Their Engineering Applications. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 762. Springer, Singapore. https://doi.org/10.1007/978-981-10-6373-2_2
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DOI: https://doi.org/10.1007/978-981-10-6373-2_2
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