Delay Compensation on Fuzzy Trajectory Tracking Control of Omni-Directional Mobile Robots

Document Type : Research Article

Authors

1 MSc Student, Department of Electrical, Biomedical and Mechatronic Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

2 MSc Student, Young Researchers and Elite Club, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

3 Assistant Professor, Department of Electrical, Biomedical and Mechatronic Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

4 Professor, Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran.

Abstract

This paper presents a delay compensator fuzzy control for trajectory tracking of omni-directional mobile robots. Fuzzy logic control (FLC) of the robots is a suitable strategy for dealing with model uncertainties, nonlinearities and disturbances.  On the other hand, in many robotic applications such as mobile robots, delay phenomenon is able to substantially deteriorate the behavior of system's performance if not considered in the controller design. In this work, a delay compensator strategy is employed in order to eliminate the influence of dead time problem. On the other hand, a discrete-time kinematic model is presented for high level control of SSL soccer robots. Also, the model uncertainties are considered as multiplicative parameters and external random disturbances are noticed as additive parameters. The simulation experiments as well as real system experiments demonstrate that the proposed method handles both constant time delay and uncertainties with a small tracking error in comparison with pure fuzzy control.

Keywords

References

[1] L. Vachhani, A. D. Mahindrakar, and K.
Sridharan. "Mobile robot navigation through a
hardware-efficient implementation for control law-based construction of generalized voronoi diagram." Mechatronics, IEEE/ASME Transactions on 16.6 (2011): 1083-1095.
[2] Altafini, Claudio. "A path-tracking criterion for an LHD articulated vehicle."The International Journal of Robotics Research 18.5 (1999): 435-441.
[3] A. KAMAGA, and Ahmed Rachid. "A simple path tracking controller for car-like mobile robots." Choice 1 (1997): 2.
[4] Altafini, Claudio. "Following a path of varying curvature as an output regulation problem." (2002).
[5] Wit, Jeff, Carl D. Crane, and David Armstrong. "Autonomous ground vehicle path tracking." Journal of Robotic Systems 21.8 (2004): 439-449.
[6] Antonelli, Gianluca, Stefano Chiaverini, and Giuseppe Fusco. "A fuzzy-logic-based approach for mobile robot path tracking." Fuzzy Systems, IEEE Transactions on 15.2 (2007): 211-221.
[7] Omid Mohareri, Rached Dhaouadi, and Ahmad B. Rad. "Indirect adaptive tracking control of a nonholonomic mobile robot via neural networks."Neurocomputing 88 (2012): 54-66.
[8] Yang, Simon X., et al. "A bioinspired neurodynamics-based approach to tracking control of mobile robots." Industrial Electronics, IEEE Transactions on 59.8 (2012): 3211-3220.
[9] Bingül, Zafer, and Oğuzhan Karahan. "A Fuzzy Logic Controller tuned with PSO for 2 DOF robot trajectory control." Expert Systems with Applications 38.1 (2011): 1017-1031.
[10] Park, Bong Seok, et al. "A simple adaptive control approach for trajectory tracking of electrically driven nonholonomic mobile robots." Control Systems Technology, IEEE Transactions on 18.5 (2010): 1199-1206.
[11] K. Shojaei, and A. M. Shahri. "Output feedback tracking control of uncertain non-holonomic wheeled mobile robots: a dynamic surface control approach."Control Theory & Applications, IET 6.2 (2012): 216-228.
[12] Zhong, Guoliang, et al. "Trajectory tracking of wheeled mobile robot with a manipulator considering dynamic interaction and modeling uncertainty."Intelligent Robotics and Applications. Springer Berlin Heidelberg, 2012. 366-375.
[13]
C. Canudas de Wit and O. J. Sordalen,
“Exponential stabilization of mobile robot with nonholonomic constraints,” IEEE Trans. Autom. Control, vol. 37, no. 11, pp. 1791–1797, Nov. 1992.
[14]
C. Samson, “Control of chained system application to path following and time-varying point-stabilization of mobile robots,” IEEE Trans. Autom. Control, vol. 40, no. 1, pp. 64–77, Jan. 1995.
[15] Zhong, Guoliang, et al. "Trajectory tracking of wheeled mobile robot with a manipulator considering dynamic interaction and modeling uncertainty."Intelligent Robotics and Applications. Springer Berlin Heidelberg, 2012. 366-375.
[16] Chwa, Dongkyoung. "Fuzzy adaptive tracking control of wheeled mobile robots with state-dependent kinematic and dynamic disturbances." Fuzzy Systems, IEEE Transactions on 20.3 (2012): 587-593.
[17] Jafar Keighobadi, and Mohammad B. Menhaj. "From nonlinear to fuzzy approaches in trajectory tracking control of wheeled mobile robots." Asian Journal of Control 14.4 (2012): 960-973.
[18] Cortes, Patricio, et al. "Delay compensation in model predictive current control of a three-phase inverter." Industrial Electronics, IEEE Transactions on 59.2 (2012): 1323-1325.
[19]
S. Zickler, T. Laue, O. Birbach, M. Wongphati, and M. Veloso, “SSLVision: The Shared Vision System for the RoboCup Small Size League”, RoboCup 2009: Robot Soccer World Cup XIII, vol. 5949, pp. 425–436, 2009.
[20]
E. Hashemi, M. Ghaffari Jadidi, O. Bakhshandeh, Trajectory planning optimization with dynamic modeling of four wheeled omni-directional mobile robots, in: Proc. of IEEE Int. Conf. on Computational Intelligence in Robotics and Automation, December 2009, pp. 272–277.
[21] Purwin, Oliver, and Raffaello D’Andrea. "Trajectory generation and control for four wheeled omnidirectional vehicles." Robotics and Autonomous Systems54.1 (2006): 13-22.
AUT Journal of Electrical Engineering
Volume 45, Issue 2 - Serial Number 2
November 2013
Pages 57-64

Files

Share

How to cite

Statistics