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Model Predictive Control for Positioning and Navigation of Mobile Robot with Cooperation of UAV

by Moustafa M. Kurdi, Imad A. Elzein, Alex K. Dadykin
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Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 6 - Number 7
Year of Publication: 2017
Authors: Moustafa M. Kurdi, Imad A. Elzein, Alex K. Dadykin
10.5120/cae2017652506

Moustafa M. Kurdi, Imad A. Elzein, Alex K. Dadykin . Model Predictive Control for Positioning and Navigation of Mobile Robot with Cooperation of UAV. Communications on Applied Electronics. 6, 7 ( Feb 2017), 17-25. DOI=10.5120/cae2017652506

@article{ 10.5120/cae2017652506,
author = { Moustafa M. Kurdi, Imad A. Elzein, Alex K. Dadykin },
title = { Model Predictive Control for Positioning and Navigation of Mobile Robot with Cooperation of UAV },
journal = { Communications on Applied Electronics },
issue_date = { Feb 2017 },
volume = { 6 },
number = { 7 },
month = { Feb },
year = { 2017 },
issn = { 2394-4714 },
pages = { 17-25 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume6/number7/703-2017652506/ },
doi = { 10.5120/cae2017652506 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-09-04T19:56:53.415514+05:30
%A Moustafa M. Kurdi
%A Imad A. Elzein
%A Alex K. Dadykin
%T Model Predictive Control for Positioning and Navigation of Mobile Robot with Cooperation of UAV
%J Communications on Applied Electronics
%@ 2394-4714
%V 6
%N 7
%P 17-25
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The purpose of navigation system is to help mobile robot in order to select an optimal and short path to reach the target. In most of these systems, GPS are used to determine the robot position. There are errors in positioning using GPS. This paper considers the problem of navigating a Mobile Robot in an unknown environment while maintaining visibility with a (movable or non-movable) target by means of Fuzzy Model Predictive Control (FMPC). The approach combines input variables from different resources such as: GPS, RVS (Robot Vision System), and QVS (Quad-copter Vision System). In this paper, a new approach based on Fuzzy Model Predictive Control (FMPC) is proposed to solve the positioning and navigation problems for mobile robot.

References
  1. D. Clarke, C. Mohtadi, and P. Tuffs, Generalised Predictive Control-Part I. The Basic Algorithm, Automatica, Vol. 23, No. 2, pages 137-148, 1987.
  2. J. B. Rawlings, Tutorial Overview of Model Predictive Control, IEEE Control Systems Magazine, Vol. 20, No. 3, June 2000, pages 38-52.
  3. A. Jadbabaie, J. Yu, and J. Hauser, “Stabilizing receding horizon control of nonlinear systems: A control Lyapunov function approach,” in Proceedings of the American Control Conference, San Diego, CA, USA, 1999, pp. 1535–1539.
  4. G. DE Nicolao, L. Magni, and R. Scattolini, “Stabilizing receding horizon control of nonlinear time-varying systems,” IEEE Transactions on Automatic Control, vol. 43, no. 7, pp. 1030–1036, 1998.
  5. R. Siegwart, ‎I. R. Nourbakhsh and D. Scaramuzza,” Introduction to Autonomous Mobile Robots “,2nd Edition,2004, ISBN: 978-0-262-01535-6.
  6. I. A. Elzein, Y. N. Petrenko, “An Integration of A Predictive Control Model and MPPT for PV Station”. International Conference on Smart Systems and Technologies, Osijek, Croatia. IEEE Trans. ISBN:978-1-5090-3718-6. pp. 275-280, 2016.
  7. Sotnikova MV. Development toolkit for solving control problems with non-linear predictive models in MATLAB Tr. V Int. Conf. "Design of engineering and scientific applications in the environment MATLAB”, 2011, p. 299-319.
  8. Veremey EI, MV Sotnikova, Plasma stabilization on the basis of the forecast of a steady linear approximation, St. Petersburg. Univ. Ser. 10. 2011, Vol. 1, p. 116-133.
  9. Sotnikova MV, Questions of stability of motion in control systems with predictive models, Bulletin of Voronezh State Technical University, 2012, p. 72-79.
  10. Huang, Y. L. and Lou, H. H. and Gong, J. P. and Edgar, T. F., 2000. Fuzzy model predictive control, IEE Transactions on Fuzzy Systems, 8(6).
  11. Passino, K. M. and Yurkovich, S., 1998. Fuzzy Control, Addison-Wesley Longman, California, 52.
  12. Nikolaou, M., Model Predictive Controllers: “A Critical Synthesis of Theory and Industrial Needs”, Houston, TX 77204-4792.
  13. Takagi, T. and Sugeno, M., 1985. Fuzzy identification of systems and its application to modeling and control, IEEE Trans. Syst., Man, Cybern.,15, 116–132.
Index Terms

Computer Science
Information Sciences

Keywords

Navigation; Fuzzy; MPC; Mobile Robot; UAV

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