International Journal of Modern Education and Computer Science (IJMECS)

ISSN: 2075-0161 (Print), ISSN: 2075-017X (Online)

Published By: MECS Press

IJMECS Vol.10, No.9, Sep. 2018

Comparative Study of Inspired Algorithms for Trajectory-Following Control in Mobile Robot

Full Text (PDF, 1112KB), PP.1-10

Views:141   Downloads:4


Basma Jumaa Saleh, Ali Talib Qasim al-Aqbi, Ahmed Yousif Falih Saedi, Lamees abdalhasan Salman

Index Terms

Trajectory-following Mobile Robot;Back-stepping Control;Kinematic Nonlinear Controller;National Instrument;Firefly Algorithm


This paper is devoted to the design of a trajectory-following control for a differentiation nonholonomic wheeled mobile robot. It suggests a kinematic nonlinear controller steer a National Instrument mobile robot. The suggested trajectory-following control structure includes two parts; the first part is a nonlinear feedback acceleration control equation based on back-stepping control that controls the mobile robot to follow the predetermined suitable path; the second part is an optimization algorithm, that is performed depending on the Crossoved Firefly algorithm (CFA) to tune the parameters of the controller to obtain the optimum trajectory. The simulation is achieved based on MATLAB R2017b and the results present that the kinematic nonlinear controller with CFA is more effective and robust than the original firefly learning algorithm; this is shown by the minimized tracking-following error to equal or less than (0.8 cm) and getting smoothness of the linear velocity less than (0.1 m/sec), and all trajectory- following results with predetermined suitable are taken into account. Stability analysis of the suggested controller is proven using the Lyapunov method.

Cite This Paper

Basma Jumaa Saleh, Ali Talib Qasim al-Aqbi, Ahmed Yousif Falih Saedi, Lamees abdalhasan Salman, "Comparative Study of Inspired Algorithms for Trajectory-Following Control in Mobile Robot", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.9, pp. 1-10, 2018.DOI: 10.5815/ijmecs.2018.09.01


[1]Kumar D N, Samalla H, Rao Ch J, Naidu S, Jose K A, Kumar B M. Position and Orientation Control of a Mobile Robot Using Neural Networks[J]. Computational Intelligence in Data Mining, Smart Innovation, Systems and Technologies, 2015, 2(13): 123-131.

[2]Seo K, Lee J S. Kinematic path-following control of mobile robot under bounded angular velocity error[J]. Advanced Robotics, 2006, 20(1): 1-23.

[3]Kanayama Y, Kimura Y, Miyazaki F, Noguchi T. A stable tracking control method for an autonomous mobile robot[J]. IEEE International Conference on Robotics and Automation, 1990: 384-389.

[4]Zain A A, Daobo W, Muhammad S, Wanyue J, Muhammad Sh. Trajectory Tracking of a Nonholonomic Wheeleed Mobile Robot Using Hybrid Controller[J]. International Journal of Modeling and Optimization, 2016, 6(3): 136-141.

[5]Kolmanovsky I, McClamroch N H. Developments in nonholonomic control problems[J]. IEEE control systems, 1995: 20-26.

[6]Blazic S. A novel trajectory-tracking control law for wheeled mobile robots[J]. Robot. Auton. Syst. 59, 2011: 1001–1007.

[7]Jiang Z P, Pomet J B. Combining backstepping and time-varying techniques for a new set of adaptive controllers[J]. IEEE Int. Conf. Decision Contr.,1994: 2207–2212.

[8]Guldner J, Utkin V I. Stabilization of nonholonomic mobile robots using Lyapunov functions for navigation and sliding mode control[J]. IEEE Int. Conf. Decision Contr., 1994: 2967–2972.

[9]Geem Z W, Kim H J, Loganathan G V. A new heuristic optimization algorithm: Harmony search[J]. SAGE Journals, ,2001, 76(2): 60-68.

[10]Adithyan T, Vasudha Sh, Gururaj B, Chandrasegar Th. Nature inspired algorithm[C]. International Conference on Trends in Electronics and Informatics (ICEI), pp. 1131 – 1134.

[11]Yang X S. Firefly Algorithms for Multimodal Optimization[J]. SAGE Journals, vol. 5792, pp. 169–178,2009.

[12]Nizar H, Basma J. Trajectory Tracking Controllers for Mobile Robot: Modeling, Design and Optimization[M]. Lambert Academic Publishing, 2016.

[13]Al-Araji A. Design of a cognitive neural predictive controller for mobile robot[M]. Ph.D. thesis, Brunel University, United Kingdom, 2012.

[14]Nizar H, Basma J. Design of a Kinematic Neural Controller for Mobile Robots based on Enhanced Hybrid Firefly-Artificial Bee Colony Algorithm[J]. AL -Khwarizmi Engineering Journal, vol. 12, no. 1, pp. 45-60 ,2016.

[15]Ye J. Adaptive control of nonlinear PID-based analogue neural network for a nonholonomic mobile robot[J]. Neurocomputing, vol. 71, no. 7, pp. 1561-1565 , 2008.

[16]Yousif Z, Hedley J, Bicker R. Design of an adaptive neural kinematic controller for a national instrument mobile robot system[J]. IEEE International Conference on Control System, Computing and Engineering, pp. 623-628, 2012.

[17]Yuan G. Tracking Control of a Mobile Robot using Neural Dynamics based Approaches[M]. Master Thesis, University of Guelph, 2001.

[18]Al-Araji A. Development of kinematic path-tracking controller design for real mobile robot via back-stepping slice genetic robust algorithm technique[J]. Arab J. Sci. Eng. vol. 39, no. 4, pp. 8825–8835, 2014.  

[19]Al-Araji A. Design of on-line nonlinear kinematic trajectory tracking controller for mobile robot based on Optimal Back-Stepping Technique[J]. Iraqi Journal of Computers, Communications, Control and Systems Engineering. vol. 2, no. 14, pp. 25-36, 2014.

[20]Xing B, Gao W J. Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms[J]. Computational Intelligence and Complexity, 2014.

[21]Yang X S. Nature-Inspired Metaheuristic Algorithms[M]. Second Edition, Luniver Press, 2010.