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Design of ANFIS Controller Based on Fusion Function for Linear Inverted Pendulum

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Proceedings of International Conference on Advances in Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 174))

Abstract

“Rule number explosion” and “adaptive weights tuning” are two main issues in the design of fuzzy control systems. To overcome the problems, a method is implemented for control of the inverted pendulum (IP) using linear fusion function based on LQR mapping, and combines it with adaptive control scheme to tune controller parameters using ANFIS. By using fusion, the output variables of the system with four dimensions are synthesized as two variables: error and variation of error. The method is applied to the approximate linear model, and the experimental results show that this method has better tracking performance, disturbance resisting performance, and robustness against model parameter perturbance.

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References

  1. Jang, J.-S.R., Sun, C.-T.: Neuro Fuzzy Modelling and Control. IEEE Proc. 83, 378–406 (1995)

    Article  Google Scholar 

  2. Han, Y., Liu, Y.: One Rod Inverted Pendulum Controller Design Based on Self-Adaptive Fuzzy PID with Fuzzy. In: Proceedings of the IEEE, 8th World Congress on Intelligent Control and Automation, China, pp. 4891–4894 (July 2010)

    Google Scholar 

  3. Googol Technology, GLIP series User’s Manual (2006)

    Google Scholar 

  4. Liu, H., Duan, F., Gao, Y.: Study on Fuzzy Control of an Inverted Pendulum System in the Simulink Environment. In: Proceedings of 2007 IEEE on Mechatronics and Automation, pp. 937–942 (August 2007)

    Google Scholar 

  5. Wang, L., Zheng, S., Wang, X., Fan, L.: Fuzzy Control of Double Inverted Pendulum Based on Information Fusion. In: IEEE International Conference on Intelligent Control and Information, pp. 327–331 (August 2010)

    Google Scholar 

  6. Han, Y., Liu, Y.: Reduced –Dimension Fuzzy Controller Design Based on Fusion Function and Application in Double Inverted Pendulum. In: IEEE International Conference on Industrial Mechatronics and Automation, vol. 2, pp. 337–340 (2010)

    Google Scholar 

  7. Ladeneva, Y.N.: Automatic estimation of parameters to reduce rule base of fuzzy control complex systems, a master thesis, Puebla, Mexico (August 2006)

    Google Scholar 

  8. Qu, Z., Xie, W., Zhou, Q.: Variable Composition Based Adaptive Fuzzy Control of Double Inverted Pendulum. In: IEEE Conference on Fuzzy System and Knowledge Discovery, vol. 2, pp. 768–772 (2010)

    Google Scholar 

  9. The Mathworks, Using Matlab version 7.10.0, The Mathworks, R2010a

    Google Scholar 

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Correspondence to Abhishek Kumar .

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Kumar, A., Mitra, R. (2013). Design of ANFIS Controller Based on Fusion Function for Linear Inverted Pendulum. In: Kumar M., A., R., S., Kumar, T. (eds) Proceedings of International Conference on Advances in Computing. Advances in Intelligent Systems and Computing, vol 174. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0740-5_45

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  • DOI: https://doi.org/10.1007/978-81-322-0740-5_45

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-0739-9

  • Online ISBN: 978-81-322-0740-5

  • eBook Packages: EngineeringEngineering (R0)

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