Abstract
The Electromagnetic Suspension (EMS) type maglev vehicles suffer from various control complexities such as strong nonlinearity, open-loop instability, parameter perturbations, which make the control of magnetic levitation system (MLS) very challenging. In actual operation, ever-changing passengers will cause the mass of vehicle body to deviate from the nominal mass of the levitation controller design, which will deteriorate the controller performance significantly. Therefore, the passenger carrying capacity of the maglev vehicle is strictly restricted, which restricts the further promotion of the maglev traffic. In this paper, an airgap control strategy based on the neural network (NN) is proposed for maglev vehicles with time-varying mass. Specifically, firstly, a nonlinear controller is proposed to guarantee the asymptotic stability of the closed-loop system. Next, to tackle the unknown or time-varying mass of vehicle body, an adaptive radial basis function (RBF) NN is utilized together with the proposed nonlinear controller. The stability analysis of the overall system is provided by Lyapunov techniques without any linearization to the original nonlinear model. Finally, a series of simulation results for the maglev vehicle are included to show the feasibility and superiority of the proposed control approach.
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References
Lee, H.W., Kim, K.C., Lee, J.: Review of maglev train technologies. IEEE Trans. Magn. 42(7), 1917–1925 (2006)
Sun, Y., Xu, J., Qiang, H., Chen, C., Lin, G.: Fuzzy H∞ robust control for magnetic levitation system of maglev vehicles based on T-S fuzzy model: design and experiments. J. Intell. Fuzzy Syst. 36(2), 911–922 (2019)
Thornton, R.D.: Efficient and affordable maglev opportunities in the United States. Proc. IEEE 97(11), 1901–1921 (2009)
Boldea, I., Tutelea, L., Xu, W., et al.: Linear electric machines, drives, and MAGLEVs: an overview. IEEE Trans. Ind. Electron. 65(9), 7504–7515 (2017)
MacLeod, C., Goodall, R.M.: Frequency shaping LQ control of maglev suspension systems for optimal performance with deterministic and stochastic inputs. IEE Proc.-Control Theory Appl. 143(1), 25–30 (1996)
Sinha, P.K., Hadjiski, L.M., Zhou, F.B., et al.: Electromagnetic suspension: new results using neural networks. IEEE Trans. Magn. 29(6), 2971–2973 (1993)
Sinha, P.K., Pechev, A.N.: Model reference adaptive control of a maglev system with stable maximum descent criterion. Automatica 35(8), 1457–1465 (1999)
Sun, Y., Qiang, H., Mei, X., et al.: Modified repetitive learning control with unidirectional control input for uncertain nonlinear systems. Neural Comput. Appl. 30(6), 2003–2012 (2017)
Wai, R.J., Chen, M.W., Yao, J.X.: Observer-based adaptive fuzzy-neural-network control for hybrid maglev transportation system. Neurocomputing 175, 10–24 (2016)
Sun, N., Fang, Y., Chen, H.: Tracking control for magnetic-suspension systems with online unknown mass identification. Control Eng. Pract. 58, 242–253 (2017)
Morales, R., Feliu, V., Sira-Ramirez, H.: Nonlinear control for magnetic levitation systems based on fast online algebraic identification of the input gain. IEEE Trans. Control Syst. Technol. 19(4), 757–771 (2011)
Zhang, M., Jing, X.: A bioinspired dynamics-based adaptive fuzzy SMC method for half-car active suspension systems with input dead zones and saturations. IEEE Trans. Cybern. (2020). https://doi.org/10.1109/tcyb.2020.2972322
Sun, Y., Xu, J., Qiang, H., Lin, G.: Adaptive neural-fuzzy robust position control scheme for maglev train systems with experimental verification. IEEE Trans. Ind. Electron. 66(11), 8589–8599 (2019)
Kusagawa, S., Baba, J., Shutoh, K., et al.: Multipurpose design optimization of EMS-type magnetically levitated vehicle based on genetic algorithm. IEEE Trans. Appl. Supercond. 14(2), 1922–1925 (2004)
Liu, C., Rong, G.: SVM α order inverse system decoupling time-varying sliding mode control of double suspension systems of machining center. China Mech. Eng. 26(5), 668–674 (2015)
Qiang, H., Li, W., Sun, Y., et al.: Levitation chassis dynamic analysis and robust position control for maglev vehicles under nonlinear periodic disturbance. J. VibroEng. 19(2), 1273–1286 (2017)
Acknowledgement
This work was supported in part by the National Key Technology R&D Program of the 13th Five-year Plan (2016YFB1200601), by the National Natural Science Foundation of China under Grant 51905380, by China Postdoctoral Science Foundation under Grant 2019M651582.
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Sun, Y., Xu, J., Zhang, W., Lin, G., Sun, N. (2020). Neural Network-Based Adaptive Control for EMS Type Maglev Vehicle Systems with Time-Varying Mass. In: Zhang, H., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2020. Communications in Computer and Information Science, vol 1265. Springer, Singapore. https://doi.org/10.1007/978-981-15-7670-6_32
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DOI: https://doi.org/10.1007/978-981-15-7670-6_32
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