Design of Hybrid Neural Fuzzy Controller for Human Robotic Leg System

  • Ekhlas H. Karam Department of Computer Engineering/ University of Al-Mustansyria
  • Ayam M Abbass Department of Computer Engineering/ University of Al-Mustansyria
  • Noor S. Abdul-Jaleel Department of Electrical Engineering/ University of Al-Mustansyria

Abstract

 In this paper, the human robotic leg which can be represented mathematically by single input-single output (SISO) nonlinear differential model with one degree of freedom, is analyzed and then a simple hybrid neural fuzzy controller is designed to improve the performance of this human robotic leg model. This controller consists from SISO fuzzy proportional derivative (FPD) controller with nine rules summing with single node neural integral derivative (NID) controller with nonlinear function. The Matlab simulation results for nonlinear robotic leg model with the suggested controller showed that the efficiency of this controller when compared with the results of the leg model that is controlled by PI+2D, PD+NID, and FPD-ID controllers.

Downloads

Download data is not yet available.

References

Kenji Kaneko, Shuuji Kajita, Fumio Kanehiro, Kazuhito Yokoi, Kiyoshi Fujiwara, Hirohisa Hirukawa, Toshikazu Kawasaki, Masaru HIRATA, and Takakatsu Isozumi, Design of Advanced Leg Module for Humanoid Robotics, Project of METI, International Conference on Robotics Automation, Washington, DC, May, 2002.

Alexander Schepelmann, Michael D. Taylor, Hartmut Geyer, Development of a Testbed for Robotic Neuromuscular Controllers, Robotics: Science and System VIII, Jan 2012.

A. Abraham et al., Neuro-Fuzzy Methods for Modeling and Identification, Recent advances in intelligent paradigm and applications, springer-verlag, Berlin Heidelbe, 2003.

Wei-Yen Wanga, Yih-Guang Leub, Tsu-Tian Lee, Output-feedback control of nonlinear systems using direct adaptive fuzzy-neural controller, Fuzzy Sets and Systems 140, 341–358, 2003.

Dr. Zs. J. Viharos, K. B. Kis, Survey on Neuro-Fuzzy Systems and their Applications in Technical Diagnostics, 13th IMEKOTC10 Workshop on Technical Diagnostics Advanced measurement tools in technical diagnostics for systems' reliability and safety, Warsaw, Poland, June 26-27, 2014.

A. Aldair and W. J. Wang, Adaptive Neuro Fuzzy Inference Controller for Full Vehicle Nonlinear Active Suspension Systems, School of Engineering and Design, Iraq J. Electrical and Electronic Engineering, Vol.6 No.2, 2010.

A. Fahmy , A.M. Abdel Ghany, Neuro-fuzzy inverse model control structure of robotic manipulators utilized for physiotherapy applications, Ain Shams Engineering Journal, 805–829, 2013.

Chun HtooAung, KhinThandarLwin, and Yin Mon Myint, Modeling Motion Control System for Motorized Robot Arm using MATLAB, World Academy of Science, Engineering and Technology 42, 2008.

Abdur Raquib Ridwan, Md. Ibnea Sina Bony and Ishtiza Ibne Azad, Performance Analysis of MPC in the Control of a Simple Motorized Nonlinear Model of a Robotic Leg, International Journal of Computer Applications (0975 – 8887), Volume 54, No. 11, September 2012.

Richard L. Shell Ernest L. Hall, Handbook of Industrial Automation, University of Cincinnati Cincinnati, Ohio, New Yourk, 2000.

Nidhi K., Simulation Studies on Hybrid Fuzzy-PI Controllers for DC Motor Control, Master thesis of Engineering in Control & Instrumentation, Maharishi Dayanand University, Rohtak Haryana, 2006.

C. Yen, F. Wen, and M. Ouyang, Nonlinear Positioning Compensator of a Novel Thin-Disc Ultrasonic Motor using Fuzzy Sliding Mode Control, International Journal of Applied Science and Engineering, Vol. 2, No. 3, pp. 257-276, 2004.

[Heikki N. Koivo, Neural Networks: Basics using MATLAB, Neural Network Toolbox, February 1, 2008.

Published
2018-04-08
How to Cite
Karam, E., Abbass, A., & Abdul-Jaleel, N. (2018). Design of Hybrid Neural Fuzzy Controller for Human Robotic Leg System. Al-Khwarizmi Engineering Journal, 14(1), 145-155. https://doi.org/10.22153/kej.2018.08.007
Section
Articles