Neural Networks Identification and Control of Mobile Robot Using Adaptive Neuro Fuzzy Inference System

Ahmed Jaber Abougarair

Abstract


This paper developed and investigates the performance of intelligent algorithms in order to stabilize the robot when it is tracking to the desired reference. One type of robot is a Two Wheeled Balancing Mobile Robot (TWBMR) that requires control for both balancing and maneuvering. Combination artificial intelligence, Neural Networks (NNs) and Fuzzy Logic Control (FLC) have been recognized as the main tools to improve the performance of coupling nonlinear robot system without using any mathematical model. The input-output data of TWBMR generated from closed loop control system is used to develop a neural network model. In this study, neural networks model can be trained offline and then transferred into a process where an adaptive online learning is carried out using Adaptive Network Based Fuzzy Inference System (ANFIS) to improve the system performance. The simulation results verify that the considered identification and control strategies can achieve favorable control performance.The ANFIS control design approach does not require an accurate model of the plant as classical controller. In addition, high-level knowledge of the system is not needed to build a set of rules as a fuzzy controller.

Keywords


ANFIS; FLC ;IUI ; NN

References


Jian Xin, Zhao Qin Guo and Tong Heng Lee, "Design and Implementation of Integral Sliding-Mode Control on an Underactuated Two-Wheeled Mobile Robot," IEEE Transactions on industrial electronics, Vol. 61, No. 7, JULY 2014.

Chenguang Yang, Zhijun Li Rongxin Cui and Bugong, "Neural Network Based Motion Control of Underactuated Wheeled Inverted Pendulum Models," IEEE Transactions on neural Networks and learning systems, Vol. 25, No. 11, November 2014.

Daniel R. Jones and Karl A. Sto, "Modeling and Stability Control of Two-Wheeled Robots in Low-Traction Environments," Department of Mechanical Engineering, University of Auckland, New Zealand, 2014.

Hongguo Niu, Niu Wang and Nan Li, "The adaptive Control Based on BP Neural Network Identification for Two Wheeled Robot, " 12th World Congress on Intelligent Control and Automation (WCICA), China ,June 12-15, 2016.

Zhang Zheng, MengTeng , "Modeling and Decoupling Control for Two-Wheeled Self-Balancing Robot," Engineering Research Center of for Metallurgical Automation and Detecting Technology of Ministry of Education Wuhan University of Science and Technology, IEEE, china, 2016.

Ahmad and Osman, "Real-Time Control System for a Two-WheeledInverted Pendulum Mobile Robot," Advanced Knowledge Applicationin Practice University Technology, Malaysia, 2015.

Steven J. Witzand, "Coordinated LEGO Segways," MSc thesis, University of New South Wales, 2009.

Martin T. Hagan, "Neural Network Design, " Oklahoma State University, Howard B. Demuth, University of Idaho, 1996.

The Math Works Inc., Neural Network Toolbox Users Guide. 2004.

Ashwani Kharola, "A PID Based ANFIS & Fuzzy Control of Inverted Pendulum on Inclined Plane (IPIP)," International Journal on Smart Sensing and Intelligent Systems, Vol. 9, No. 2, June 2016.

Castro J. Castillo and Melin, P., "An Interval Type-2 Fuzzy Logic Toolbox for Control Applications," Proceedings of the IEEE International Conference on Fuzzy Systems, FUZZ-IEEE, London, UK, 2007.

Shwani Kharola, "A Comparative Analysis of Fuzzy Based Hybrid ANFIS Controller for Stabilization and Control of Non-linear Systems," International Journal of Soft Computing, Mathematics and Control (IJSCMC), Vol. 4, No. 4, November 2015.

J.S.R. Jang, and C.T. Sun, "Neuro-Fuzzy Modeling and Control," Proc. IEEE, Vol. 83, No. 3, pp. 378-406, Mar. 1995.

S. F. Kodad, B V. Sankar Ram, "Modeling, Design & Simulation of an Adaptive Neuro Fuzzy Inference System (ANFIS) for Speed Control of Induction Motor", International Journal of Computer Applications, Vol. 6, No.12, September 2010.

Ahmed J. Abougarair, "Balancing and trajectory tracking control of mobile robot using robust intelligent system based on 3D Animation," Ph. D dissertation," Alneelain University, Sudan, 2018.




DOI: http://doi.org/10.11591/ijra.v9i4.pp%25p
Total views : 27 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

IJRA Visitor Statistics

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.