Real-time Implementation of a Neuro-Fuzzy Control System for the Inverted Pendulum

Authors

  • Arturo Rojas-Moreno Facultad de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Ingeniería, Lima - Perú
  • César Nuñez-Ocola Facultad de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Ingeniería, Lima - Perú
  • Fernando Merchan-Gordillo Facultad de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Ingeniería, Lima - Perú
  • Luis E. Córdova-Sosa Facultad de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Ingeniería, Lima - Perú

DOI:

https://doi.org/10.21754/tecnia.v11i2.518

Keywords:

Artificial Intelligence, Design, Controllers, Fuzzy Logic, Takagi-Kosko-Sugeno, Neuro-Fuzzy, ANFIS, Inverted Pendulum , Balance of a Rod , Cart, Pendulum

Abstract

This article presents the design and implementation procedure of a TKS (Takagi-Kosko-Sugeno) fuzzy controller that uses the ANFIS (Adaptive Neuro-Fuzzy Inference System) tuning technique. The developed methodology is applied to a classic case study, but with particularly attractive characteristics to test any type of controller: the Inverted Pendulum System (INS). The SPI comprises a rod attached at one end to a pivot mounted on a carriage that moves in a straight line on rails. The objective
Control is to keep the rod in a vertical position by means of a force applied to the carriage. The design procedure requires: modeling the SPI, selecting a control strategy according to the input/output variables of the model, selecting the control strategy according to the types of control loops on the variables, and, the design of the fuzzy controller using ANFIS to optimize its performance. After the system has been simulated, the real-time implementation is carried out using a HW/SW (Hardware/Software) interface. The control software is written in C++. The experimental results obtained validate the developed design procedure.

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References

[1} . César Nuñez Ocola, "Controlador de Lógica Difusa Implementado en PLC's...", Tésis de Grado, UNSA, 1996.

[2] . M. J. Patyra D.M. Mlynek "Fuzzy Logic: Implementation and Aplications"

[3] . Enzo Gandolfi. Alessandro Gabrielli. Massimo Masetti. "Introduction to the Fuzzy Logic" Apr95 AIHNP Conference, Pisa.

[4] . Kosko, Bart, "Neural Networks and Fuzzy Systems", Englewood Cliffs, N.J.: Prentice Hall. Inc. 1992.

[5] . Jan Jantzen, "Design Of Fuzzy Controllers", Technical University of Denmark, Department of Automation, August 1998.

[6] . Jan Jantzen, "Neurofuzzy Modelling", Technical University of Denmark, Department of Automation, October 1998.

[7] . Takagi, T. and Sugeno, M. (1985). "Fuzzy identification of systems and its applications to modeling and control", 116-132. Tagkagi & Sugeno 1986.

[8] . Jang, Sun & Mizutani, Neuro-fuzzy modeling and control, 1997.

[9] . Jan Jantzen, "Analysis Of A Pendulum Problem", Technical University of Denmark, Department of Automation, Tech. report no 98-E 863 (cartball), 19 Aug 1998.

[10] . Arturo Rojas Moreno, Control Avanzado-Diseño y Aplicaciones en Tiempo Real, Publicación Independiente, Lima, 2001.

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Published

2001-12-01

How to Cite

[1]
A. Rojas-Moreno, C. Nuñez-Ocola, F. Merchan-Gordillo, and L. E. Córdova-Sosa, “Real-time Implementation of a Neuro-Fuzzy Control System for the Inverted Pendulum”, TEC, vol. 11, no. 2, Dec. 2001.

Issue

Vol. 11 No. 2 (2001)

Section

Articles

License

Copyright (c) 2001 TECNIA

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