Fuzzy Adaptive Application for Control of Single Wheel Mobile Robot (SWMR)

  • Ashwani Kharola Department of Mechanical Engineering, Graphic Era (Deemed to be University), Dehradun, India
  • Pravin Patil Department of Mechanical Engineering, Graphic Era (Deemed to be University), Dehradun, India
Keywords: SWMR, Fuzzy Logic, ANFIS, Matlab, Simulink, Membership Function


Single Wheeled Mobile Robot (SWMR) comprises of a robot chassis mounted on a single wheel and capable of performing 360° orientation rotation while maintaining its stable position. The objective is to control robot orientation and wheel motion at desired location. The single wheel makes the system more difficult to control as compared to double wheel robot. This paper presents the control of highly non-linear, multivariable and complex SWMR system using fuzzy and ANFIS controllers. The fuzzy controllers were used to train the ANFIS controllers using gbell membership functions. A Matlab-Simulink model of the system was initially developed from mathematical equations derived using Newton's second law of motion. The simulation results are shown with the help of graphs and tables which proves the superiority of fuzzy technique over ANFIS approach. The results showed that fuzzy controllers were able to stabilize the SWMR system within 4.5 sec. The steady state error for both the controllers shows an excellent response. The maximum overshoot for chassis controller are within specified limits whereas it needs to be lowered for wheel controller. The performance parameters i.e. settling time, maximum overshoot and steady state error further highlights the effectiveness of both the controllers.


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Al-Mamun, A., & Zhu, Z. (2010). PSO-optimized fuzzy logic controller for a single wheel robot. Book Title Trends in Intelligent Robotics, 103,330-337, DOI: 10.1007/978-3-642-15810-0_42.

Buratowski, T., Cieslek, P., Giergiel, M., & Uhl, T. (2012). A self-stabilising multipurpose single-wheel robot. Journal of Theoretical and Applied Mechanics,50(1), 99-118.

Castillo, O., Aguilar, L. T., & Cardenas, S. (2006). Fuzzy logic tracking control for unicycle mobile robots. Engineering Letters,13(2), 1-5.

Cieslak,P., Buratowski, T., Uhl, T., & Giergiel, M. (2011). The mono-wheel robot with dynamic stabilisation. Robotics and Autonomous Systems,59(9), 611-619, DOI: 10.1016/j.robot.2011.05.002.

Das, T., & Kar, I. N. (2006). Design and implementation of an adaptive fuzzy logic-based controller for wheeled mobile robots. IEEE Transactions on Control Systems Technology, 14(3), 501-510.

Ha, M. S., & Jung, S. (2015). Angle compensation by fuzzy logic for balancing a single-wheel mobile robot. Proceedings of 10th IEEE Asian Control Conference (ASCC),1-4, Kota Kinabalu, DOI: 10.1109/ASCC.2015.7244524.

Huang, C. N. (2010). The development of self-balancing controller for one wheeled vehicle. Engineering,2(4), 212-219.

Islam, M. S., Azim, M. A., Jahan, M. S., & Othman, M. (2006). Design and synthesis of mobile robot controller using fuzzy logic. Proceedings of IEEE International Conference on Semiconductor Electronics, 825-829, Kuala Lumpur,DOI: 10.1109/SMELEC.2006.380752.

Jae-oh, L., In-Woo, H., & Jang-Myung, L. (2011). Fuzzy sliding modecontrol of unicycle robot. Proceedings of 8th IEEE International Conference on Ubiquitous Robots and Ambient Intelligence (URAI),521-524, Incheon,DOI: 10.1109/URAI.2011.6145875.

Jang, J. S. R. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685.

Jin, H., Hwang, J., & Lee, J. (2011). A balancing control strategy for a one wheel pendulum robot based on dynamics model decomposition: simulations and experiments. IEEE/ASME Transactions on Mechatronics,16(4), 763-768.

Kayacan, E., Bayraktaroglu, Z. Y., & Saeys, W. (2012). Modeling and control of a spherical rolling robot: a decoupled dynamics approach. Robotica, 30(4), 671-680.

Kayacan, E., Ramon, H., & Saeys, W. (2012). Adaptive neuro-fuzzy control of a spherical rolling robot using sliding-mode-control-theory-based online learning algorithm. IEEE Transaction on Cybernetics, 43(1), 170-179.

Lauwers, T. B., Kantor,G. A., & Hollis, R. L. (2006). A Dynamically stable single-wheeled mobile robot with inverse mouse-ball drive. Proceedings of IEEE International Conference on Robotics and Automation, 2884-2889, Orlando, Florida.Li, Y., Lee, J. O., & Lee, J. (2012). Attitude control of the Unicycle robot using fuzzy-sliding mode control. Book title Intelligent Robotics and Applications, 7508, 62-72, DOI: 10.1007/978-3-642-33503-7_7.

Li, Y., Ren, X., & Liu, J. (2012). A new fuzzy control and dynamic modeling of bicycle robot. Proceedings of 4th IEEE International Conference on Intelligent Human-machine systems and Cybernetics, 2, 53-58, Nanchang,DOI: 10.1109/IHMSC.2012.109.

Mary, P. M., & Marimuthu, N. S.(2009). Minimum time swing up and stabilization of rotary inverted pendulum using pulse step control. Iranian Journal of Fuzzy Systems,6(3), 1-15.

Moraga, C. (2005). Introduction to fuzzy logic. Facta Universitatis, 18(2), 319-328.

Nagarajan, U., Kim, B., & Hollis, R. (2012). Planning in High-dimensional shape space for a single-wheeled balancing mobile robots with arms. Proceedings of IEEE International Conference on Robotics and Automation (ICRA), 130-135, Saint Paul,DOI: 10.1109/ICRA.2012.6225065.

Otani, T., Urakubo, T., Maekawa, S., Tamaki, H., & Tada, Y. (2006). Position and attitude control of a spherical rolling robot equipped with a gyro. Proceedings of 9th IEEE Workshop on Advanced Motion Control,416-421, Istanbul,DOI: 10.1109/AMC.2006.1631695.

Park, J. H., & Jung, S. (2013). Development and control of a single-wheel robot: practical mechatronics approach. Mechatronics, 23(6), 594-606, DOI: 10.1016/j.mechatronics.2013.05.010.

Peng, Y. F., Chiu, C. H., Tsai, W. R., & Chou, M. H. (2009). Design of an omni-directional spherical robot: using fuzzy control. In Proceedings of the International Multi Conference of Engineers and Computer Scientists, 1, 18-20.

Pfister, M., & Rojas, R. (1994). Hybrid learning algorithm for feed forward neural networks. Book Title Fuzzy Logic,61-68,DOI: 10.1007/978-3-642-79386-8_8.

Rashid, M. K. (2007). Simulation of intelligent single wheel mobile robot. International Journal of Advanced Robotic Systems, 4(1), 73-80.

Ruan, X., & Xie, W. (2015). Lateral dynamic modelling and control of a single wheel robot based on airflow flywheel. Proceedings of IEEE International Conference on Mechatronics and Automation, 2192-2196, Beijing,DOI: 10.1109/ICMA.2015.7237826.

Sivanandam, S. N., Sumathi, S., & Deepa, S. N. (2007). Introduction to fuzzy logic using MATLAB, 1, Springer. Xu, Y.,& Au, S. K. W. (2004). Stabilization and path following of a single wheel robot. IEEE/ASME Transaction on Mechatronics,9(2), 407-419.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control,8(3), 338-353.

Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-II. Information Sciences,8(4), 301-357, DOI: 10.1016/0020-0255(75)90046-8.

Zhen, Z., Al-Mamun, A., & Naing, M. P. (2009). Control-centric simulator for mechatronics design. International Journal on Smart Sensing and Intelligent Systems, 2(2), 190-199.