Abstract
System identification is the basis of designing control system. The bicycle robot is an under-actuated, non-linear, non-integrated system with lateral instability, it’s two wheels are longitudinal and has non-sliding contact with the ground, meanwhile it’s dynamic characteristics are complicated. So it is very difficult to set up more precise dynamics model. While precise model of complex system often requires more complex control design and calculation. In this paper, linear ARX model and nonlinear ANFIS model are proposed. The identifications of bicycle robot system are completed through the data of handlebar angle and those of inclination angle which are gathered when bicycle robot is stable. Simulation result by ANFIS based on T-S model could be very similar to the actual test data of bicycle robot sysytem, and it’s identification precision is higher than that of linear ARX model. The obtained conclusions of fuzzy inference between input and output by above identificaton methods can provide some reference value for effective control on bicycle robot system in future.
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References
Fu-Cai, L.: Fuzzy Model Identification and Application of Nonlinear Systems. National Defense Industry Press, Beijing (2006)
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its application to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics 51(1), 116–132 (1985)
Xiu-ying, L., Zhi-gang, H.: Advances in Nonlinear System Identification. Techniques of Automation & Application 23(4), 5–7 (2004)
Zhi-Xiang, H., He-Qing, L.: Nonlinear System Identification Based on Adaptive Neural Fuzzy Inference System. In: 2006 International Conference on Communications, Circuits and Systems Proceedings, vol. 3, pp. 2067–2069 (2006)
Jang, J.-S.: ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Trans. Syst., Man., Cybern. 15, 116–132 (1985)
Rastegar, F., Araabi, B.N., Lucast, C.: An evolutionary fuzzy modeling approach for ANFIS architecture. In: The 2005 IEEE Congress on Evolutionary Computation, vol. 3, pp. 2182–2189 (2005)
Yue, J., Liu, J., Liu, X., Tan, W.: Identification of nonlinear system based on ANFIS with subtractive clustering. In: Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China, pp. 1852–1856 (2006)
Li-Wei, D., Zhi-Hua, W., Zhi-Wei, X.: Identification Research of the Piezoelectric Smart Structure System of Aircraft Wing Based on ARX Model. Piezoelectrics & Acoustooptics 30(6), 760–762 (2008)
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Yu, X., Wei, S., Guo, L. (2010). Nonlinear System Identification of Bicycle Robot Based on Adaptive Neural Fuzzy Inference System. In: Wang, F.L., Deng, H., Gao, Y., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2010. Lecture Notes in Computer Science(), vol 6319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16530-6_45
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DOI: https://doi.org/10.1007/978-3-642-16530-6_45
Publisher Name: Springer, Berlin, Heidelberg
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