Implementation of Mamdami fuzzy control on a multi-DOF two-wheel inverted pendulum robot

Yubai  Liu; Xueshan  Gao; Fuquan  Dai

doi:10.3934/dcdss.2015.8.1251

Article Contents

2015, Volume 8, Issue 6: 1251-1266. Doi: 10.3934/dcdss.2015.8.1251

This issue Previous Article Research on the optimal initial shunt strategy of Jiuzhaigou based on the optimization model Next Article Application of support vector machine model in wind power prediction based on particle swarm optimization

Implementation of Mamdami fuzzy control on a multi-DOF two-wheel inverted pendulum robot

1.
NO.5 Zhongguancun South Street, Beijing Institute of Technology, Beijing 100081
2.
NO.3 Xueyaun Road Fujian Univerity of Technology Xueyuan, Fuzhou 350118, Fujian

Received: June 30, 2015

Revised: August 31, 2015

Published: November 2015

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Abstract

These days a Two-wheel inverted pendulum (TWIP) robot attracts public attention as it is an efficient ergonomics and easy to operate by nuance personals. Furthermore it has attractive design features like compact in size and zero turning radius. However, the traditional TWIP robots have to change its posture to reach the desired speedup and deceleration by changing the robot posture forward and backward make it difficult to control its motion process. Thus, this paper presents here the Mamdami fuzzy control logic to overcome the motion control of Multi-DOF TWIP robot and make its motion smooth and steady control. By introducing two additional DOFs the slider and the swinging configuration, the robot can maintain its vertical posture even climbing and descending on slopes. To validate the robustness of the proposed method, classic PID controller is introduced for comparison in simulations and experiments. The simulation results demonstrate the effectiveness of the system design and the better performance in robustness over classic PID control strategy. Finally, the control scheme is implemented on the practical self-designed hardware.

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Mathematics Subject Classification: 93C42.

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References

[1]	A. Ahmadi, H. A. Rahim and R. A. Rahim, Optimization of a self-tuning PID type fuzzy controller and a PID controller for an inverted pendulum, Journal of Intelligent & Fuzzy Systems, 26 (2014), 1987-1999.
[2]	M. Alarfaj and G. Kantor, Centrifugal force compensation of a two-wheeled balancing robot, in 2010 11th International Conference on Control Automation Robotics & Vision (ICARCV 2010), Piscataway 2010, 2333-2338.doi: 10.1109/ICARCV.2010.5707337.
[3]	M. Bruning, F. Heinemann, W. Schonewolf and J. Kruger, Design and implementation of a Kalman state estimator for balancing of uniaxial vehicles for goods transport, in 2013 IEEE International Conference on Mechatronics (ICM), 2013, 260-266.doi: 10.1109/ICMECH.2013.6518546.
[4]	L. J. Butler and G. Bright, Control strategy for a mobile self-balancing materials handling platform, Journal of Engineering, Design and Technology, 8 (2010), 6-27.doi: 10.1108/17260531011034628.
[5]	L. Chaoquan, G. Xueshan, H. Qiang, D. Fuquan, S. Jie and B. Yang, et al., A coaxial couple wheeled robot with T-S fuzzy equilibrium control, Industrial Robot, 38 (2011), 292-300.
[6]	C.-H. Chiu, Y.-W. Lin and C.-H. Lin, Real-time control of a wheeled inverted pendulum based on an intelligent model free controller, Mechatronics, 21 (2011), 523-533.doi: 10.1016/j.mechatronics.2011.01.010.
[7]	F. Dai, X. Gao, S. Jiang, Y. Liu and J. Li, A multi-DOF two wheeled inverted pendulum robot climbing on a slope, in Robotics and Biomimetics (ROBIO), 2014 IEEE International Conference on, 2014, 1958-1963.doi: 10.1109/ROBIO.2014.7090623.
[8]	D. Feng, J. Huang, Y. Wang, X. Gao, T. Matsuno and T. Fukuda, et al., Optimal braking control for UW-Car using sliding mode, in ROBIO 2009, Guilin 2009, 117-122.
[9]	D. Feng, J. Huang, Y. Wang, T. Matsuno, T. Fukuda and K. Sekiyama, Modeling and control of a novel narrow vehicle, in 2010 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2010, 1130-1135.
[10]	S. M. Goza, R. O. Ambrose, M. A. Diftler and I. M. Spain, Telepresence control of the NASA/DARPA Robonaut on a mobility platform, in 2004 Conference on Human Factors in Computing Systems, Vienna, 2014, 623-629.doi: 10.1145/985692.985771.
[11]	F. Grasser, A. D'Arrigo, S. Colombi and A. C. Rufer, JOE: a mobile, inverted pendulum, IEEE Transactions on Industrial Electronics, 49 (2002), 107-114.doi: 10.1109/41.982254.
[12]	Z.-Q. Guo, J.-X. Xu and T. Heng Lee, Design and implementation of a new sliding mode controller on an underactuated wheeled inverted pendulum, Journal of the Franklin Institute, 351 (2014), 2261-2282.doi: 10.1016/j.jfranklin.2013.02.002.
[13]	H. Jian, D. Feng, T. Fukuda and T. Matsuno, Modeling and velocity control for a novel narrow vehicle based on mobile wheeled inverted pendulum, IEEE Transactions on Control Systems Technology, 21 (2013), 1607-1617.
[14]	S. Jung and S. S. Kim, Control experiment of a wheel-driven mobile inverted pendulum using neural network, Control Systems Technology, IEEE Transactions on, 16 (2008), 297-303.
[15]	D. Kamen, Segway, Available: http://www.segway.com/
[16]	A. Ko, H. Y. K. Lau and T. L. Lau, SOHO security with mini self-balancing robots, Industrial Robot, 32 (2005), 492-498.
[17]	L. Mao, J. Huang, F. Ding, T. Fukuda and T. Matsuno, Modeling and control for UW-Car in rough terrain, in 10th World Congress on Intelligent Control and Automation, WCICA 2012, Beijing, 2012, 3747-3752.doi: 10.1109/WCICA.2012.6359097.
[18]	S. Nagaya, T. Morikawa, I. Takami and C. Gan, Robust LQ control for parallel wheeled inverted pendulum, in Control Conference (AUCC), Australian, 2013, 189-194.doi: 10.1109/AUCC.2013.6697271.
[19]	A. N. K. Nasir, M. A. Ahmad, R. Ghazali and N. S. Pakheri, Performance comparison between fuzzy logic controller (FLC) and PID controller for a highly nonlinear two-wheels balancing robot, in 2011 First International Conference on Informatics and Computational Intelligence (ICI), Bandung, 2011, 176-181.doi: 10.1109/ICI.2011.37.
[20]	K. Pathak, J. Franch and S. K. Agrawal, Velocity and position control of a wheeled inverted pendulum by partial feedback linearization, Robotics, IEEE Transactions on, 21 (2005), 505-513.doi: 10.1109/TRO.2004.840905.
[21]	N. Jin Seok, L. Geun Hyeong, C. Ho Jin and J. Seul, Robust control of a mobile inverted pendulum robot using a RBF neural network controller, in 2008 IEEE International Conference on Robotics and Biomimetics, Piscataway, 2008, 1932-1937.
[22]	A. Shimada and N. Hatakeyama, High-speed motion control of wheeled inverted pendulum robots, in ICMA 2007, 2007, 1-6.doi: 10.1109/ICMECH.2007.4280028.
[23]	L. Shui-Chun, T. Ching-Chih and H. Hsu-Chih, Adaptive robust self-balancing and steering of a two-wheeled human transportation vehicle, Journal of Intelligent & Robotic Systems, 62 (2011), 103-123.
[24]	T. Takei, R. Imamura and S. Yuta, Baggage transportation and navigation by a wheeled inverted pendulum mobile robot, IEEE Transactions on Industrial Electronics, 56 (2009), 3985-3994.doi: 10.1109/TIE.2009.2027252.
[25]	J.-X. Xu, Z.-Q. Guo and T. H. Lee, Design and implementation of a Takagi-Sugeno-type fuzzy logic controller on a two-wheeled mobile robot, Industrial Electronics, IEEE Transactions on, 60 (2013), 5717-5728.doi: 10.1109/TIE.2012.2230600.
[26]	K. Yeon Hoon, K. Soo Hyun and K. Yoon Keun, Dynamic analysis of a nonholonomic two-wheeled inverted pendulum robot, Journal of Intelligent and Robotic Systems: Theory and Applications, 44 (2005), 25-46.
[27]	L. Zhijun and X. Chunquan, Adaptive fuzzy logic control of dynamic balance and motion for wheeled inverted pendulums, Fuzzy Sets and Systems, 160 (2009), 1787-1803.doi: 10.1016/j.fss.2008.09.013.