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ANFIS based system identification of underactuated systems

  • Ishan Chawla und Ashish Singla EMAIL logo
Veröffentlicht/Copyright: 14. Juli 2020
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Abstract

In this work, the effectiveness of the adaptive neural based fuzzy inference system (ANFIS) in identifying underactuated systems is illustrated. Two case studies of underactuated systems are used to validate the system identification i. e., linear inverted pendulum (LIP) and rotary inverted pendulum (RIP). Both the systems are treated as benchmark systems in modeling and control theory for their inherit nonlinear, unstable, and underactuated behavior. The systems are modeled with ANFIS using the input-output data acquired from the dynamic response of the nonlinear analytical model of the systems. The dynamic response of the ANFIS model is simulated and compared to the nonlinear mathematical model of the inverted pendulum systems. In order to check the effectiveness of the ANFIS model, mean square error is used as the performance index. From the obtained simulation results, it has been perceived that the ANFIS model performed satisfactorily within the trained operating range while a minor deviation is seen outside the trained operating region for both the case studies. Furthermore, the experimental validation of the of the proposed ANFIS model is done by comparing it with the experimental model of the rotary inverted pendulum. The obtained results show that the response of ANFIS model is in close agreement to the experimental model of the rotary inverted pendulum.


Corresponding author: Ashish Singla, Department of Mechanical Engineering, Thapar Institute of Engineering and Technology, Patiala, 147004, India, E-mail:

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Employment or leadership: None declared.

  4. Honorarium: None declared.

  5. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

[1] L. Ljung, System Identification: Theory for the User, PTR Prentice Hall Information and System Sciences Series, New Jersey, Prentice-Hall, 1999.Suche in Google Scholar

[2] E. J. Davison, Benchmark Problems for Control System Design, IFAC Theory Commitee, Montreal, Canada, 1990.Suche in Google Scholar

[3] W. Younis, M. Abdelati, “Design and implementation of an experimental segway model,” in AIP Conference Proceedings, AIP, 2009.10.1063/1.3106501Suche in Google Scholar

[4] A. Elhasairi and A. Pechev, “Humanoid robot balance control using the spherical inverted pendulum mode,” Front. Robot. AI, vol. 2: p. 21, 2015, https://doi.org/10.3389/frobt.2015.00021.Suche in Google Scholar

[5] C. W. Anderson, “Learning to control an inverted pendulum using neural networks,” IEEE Contr. Syst. Mag., vol. 9, no. 3, pp. 31–37, 1989, https://doi.org/10.1109/37.24809.Suche in Google Scholar

[6] N. Sun, Y. Wu, H. Chen, and Y. Fang, “Antiswing cargo transportation of underactuated tower crane systems by a nonlinear controller embedded with an integral term,” IEEE Trans. Autom. Sci. Eng., vol. 16, no. 3, pp. 1387–1398, 2019, https://doi.org/10.1109/tase.2018.2889434.Suche in Google Scholar

[7] H. Chen and N. Sun, “Nonlinear control of underactuated systems subject to both actuated and unactuated state constraints with experimental verification,” IEEE Trans. Ind. Electron., vol. 67, no. 9, pp. 7702–7714, 2019, https://doi.org/10.1109/tie.2019.2946541.Suche in Google Scholar

[8] H. Chen, B. Xuan, P. Yang and H. Chen, “A new overhead crane emergency braking method with theoretical analysis and experimental verification,” Nonlinear Dynam., vol. 98, no. 3, pp. 2211–2225, 2019, https://doi.org/10.1007/s11071-019-05318-6.Suche in Google Scholar

[9] Y. Liu, H. Yu, S. Wane, and T. Yang, “On tracking control of a pendulum-driven cart-pole underactuated system,” Int. J. Model. Ident. Contr., vol. 4, no. 4, pp. 357–372, 2008, https://doi.org/10.1504/IJMIC.2008.021476.Suche in Google Scholar

[10] C. C. Tsai, H. C. Huang, and S. C. Lin, “Adaptive neural network control of a self-balancing two-wheeled scooter,” IEEE Trans. Ind. Electron., vol. 57, no. 4, pp. 1420–1428, 2010, https://doi.org/10.1109/tie.2009.2039452.Suche in Google Scholar

[11] I. Chawla, V. Chopra and A. Singla, “Performance comparison of PID and ANFIS controller for stabilization of x and xy inverted pendulums,” in International Conference on Intelligent Systems Design and Applications, 2018, pp. 182–192.10.1007/978-3-030-16657-1_17Suche in Google Scholar

[12] I. Chawla and A. Singla, “Real-time control of a rotary inverted pendulum using robust LQR-based ANFIS controller,” Int. J. Nonlinear Sci. Numer. Simulat., vol. 19, no. 3–4, pp. 379–389, 2019, https://doi.org/10.1515/ijnsns-2017-0139.Suche in Google Scholar

[13] G. Ronquillo, G. J. Ríos-Moreno, A. Gómez-Espinosa, L. A. Morales-Hernández, and M. Trejo-Perea, “Nonlinear identification of inverted pendulum system using volterra polynomials,” Mech. Based Design Struct. Mach, vol. 44, no. 1-2, 2016. pp. 87–99. https://doi.org/10.1080/15397734.2015.1028551.Suche in Google Scholar

[14] J. S. Jang, “ANFIS: adaptive-network-based fuzzy inference system,” IEEE Trans. Syst. Man Cybern., 1993, vol. 23, no. 3, pp. 665–685, https://doi.org/10.1109/21.256541.Suche in Google Scholar

[15] M. A. Denai, F. Palis and A. Zeghbib, “ANFIS based modelling and control of non-linear systems: a tutorial,” IEEE International Conference on Systems, Man and Cybernetics, vol. 4, 2004, p. 3433–3438, https://doi.org/10.1109/ICSMC.2004.1400873.Suche in Google Scholar

[16] I. Chawla and A. Singla, “System identification of an inverted pendulum using adaptive neural fuzzy inference system,” in Harmony Search and Nature Inspired Optimization Algorithms, Singapore, Springer, 2018, pp. 809–817. https://doi.org/10.1007/978-981-13-0761-4_77.Suche in Google Scholar

[17] A. Kathpal and A. Singla, “SimMechanics™ based modeling, simulation and real-time control of rotary inverted pendulum,” in 11th International Conference on Intelligent Systems and Control, Coimbatore, India, IEEE, 2017, pp. 166–172. https://doi.org/10.1109/ISCO.2017.7855975.Suche in Google Scholar

[18] T. Takagi and M. Sugeno, “Derivation of fuzzy control rules from human operator’s control actions,” in Proceedings of the IFAC Symposium on Fuzzy Information, Knowledge Representation and Decision Analysis, 1983. https://doi.org/10.1016/S1474-6670(17)62005-6.Suche in Google Scholar

Received: 2018-01-04
Accepted: 2020-04-20
Published Online: 2020-07-14
Published in Print: 2020-11-18

© 2020 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

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  3. ANFIS based system identification of underactuated systems
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