Intelligent Path Tracking Hybrid Fuzzy Controller for a Unicycle-Type Differential Drive Robot
Authors: Abdullah M. Almeshal, Mohammad R. Alenezi, Muhammad Moaz
Abstract:
In this paper, we discuss the performance of applying hybrid spiral dynamic bacterial chemotaxis (HSDBC) optimisation algorithm on an intelligent controller for a differential drive robot. A unicycle class of differential drive robot is utilised to serve as a basis application to evaluate the performance of the HSDBC algorithm. A hybrid fuzzy logic controller is developed and implemented for the unicycle robot to follow a predefined trajectory. Trajectories of various frictional profiles and levels were simulated to evaluate the performance of the robot at different operating conditions. Controller gains and scaling factors were optimised using HSDBC and the performance is evaluated in comparison to previously adopted optimisation algorithms. The HSDBC has proven its feasibility in achieving a faster convergence toward the optimal gains and resulted in a superior performance.
Keywords: Differential drive robot, hybrid fuzzy controller, optimization, path tracking, unicycle robot.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1100316
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2625References:
[1] Almeshal, A. M., Goher, K. M., &Tokhi, M. O. (2013a). Dynamic modelling and stabilization of a new configuration of two-wheeled machines. Robotics and Autonomous Systems, 61(5), 443–472.
[2] Ti-Chung Lee, Kai-Tai Song, Ching-Hung Lee, and Ching-Cheng Teng, (2001). Tracking control of unicycle-modeled mobile robots using a saturation feedback controller. IEEE Transactions on Control Systems Technology, 9(2), pp.305-318.
[3] Astudillo, L.,Castillo, O., L. Aguilar, A. Alanis and J. Soria, 'Intelligent Control of an Autonomous Mobile Robot using Type2 Fuzzy Logic', Engineering Letters, vol. 13, no. 2, pp. 565-570, 2006.
[4] Soetanto, D., Lapierre, L., Pascoal, A., "Adaptive, non-singular pathfollowing control of dynamic wheeled robots," Decision and Control, 2003. Proceedings. 42nd IEEE Conference on , vol.2, no., pp.1765,1770 Vol.2, 9-12 Dec. 2003
[5] Almeshal, A. M., Goher, K. M., Tokhi, M. O., Sayidmarie, O., &Agouri, S. A. (2012a). Hybrid fuzzy logic control approach of a two wheeled double inverted pendulum like robotic vehicle. Adaptive Mobile Robotics - Proceedings of the 15th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2012 (pp. 681–688).
[6] Agouri, S. A., Tokhi, O., Almeshal, A., Sayidmarie, O., &Goher, K. M. (2013). Modelling and control of two-wheeled vehicle with extendable intermediate body on an inclined surface. Proceedings of the IASTED International Conference on Modelling, Identification and Control (pp. 388–393).
[7] Almeshal, A. M., Tokhi, M. O., &Goher, K. M. (2012b). Robust hybrid fuzzy logic control of a novel two-wheeled robotic vehicle with a movable payload under various operating conditions. Proceedings of the 2012 UKACC International Conference on Control, CONTROL 2012 (pp. 747–752).
[8] Almeshal, A. M., Goher, K. M., Nasir, A. N. K., Tokhi, M. O., &Agouri, S. A. (2013b). Hybrid spiral dynamic bacterial chemotaxis optimisation for hybrid fuzzy logic control of a novel two wheeled robotic vehicle. Nature-Inspired Mobile Robotics: Proceedings of the 16th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2013 (pp. 179–188).
[9] Nasir, A. N. K., Tokhi, M. O., Ghani, N. M., & Ahmad, M. A. (2012). A novel hybrid spiral dynamics bacterial chemotaxis algorithm for global optimization with application to controller design. UKACC International Conference onControl (CONTROL 2012) (pp. 753–758).
[10] Tamura, K., & Yasuda, K. (2011). Primary study of spiral dynamics inspired optimization. IEEJ Transactions on Electrical and Electronic Engineering, 6(S1), S98–S100.
[11] Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems, 22(3), 52–67.