Adaptive Back-Stepping Control for Wheeled Robot with Uncertainty

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Problems trajectory tracking of wheeled robot with uncertainty were studied. The paper proposes an back-stepping PID control algorithms base on Lyapunov theory. Mode was established base on the assumed modes method, Lagrange principle and momentum conservation. The controller is educed by back-stepping design. Complicated system is decompounded to some ordinary subsystem. Control system stability is ensured base on Lyapunov theory. The simulation resulted show that controller can improves the control accuracy and the asymptotic convergence of tracking error. Even bigger errors of beginning are appeared. The presented back-stepping PID controller can reach good control precision.

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850-854

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September 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] Lin C K. Non-singular terminal sliding model control of robot manipulators using fuzzy wavelet networks[J]. IEEE Trans. Fuzzy Syst, 2009, 160(12): 1765-1786.

DOI: 10.1016/j.fss.2008.09.010

Google Scholar

[2] Yoo B K, Ham W C. Adaptive control of robot manipulator using fuzzy compensator[J]. IEEE Trans. Fuzzy Syst, 2000, 8(2): 186-199.

DOI: 10.1109/91.842152

Google Scholar

[3] Dubowsky S, Papadopoulos E G. The kinematics, dynamics and control of free-flying space robotic systems[J]. IEEE Trans. on Robotics and Automation , 1993 , 9 (5): 531-543.

DOI: 10.1109/70.258046

Google Scholar

[4] Cheah C C, Kawamura S, Arimoto S. ,et al.H∞ tuning for task-space feedback control of robot with uncertain Jacobian matrix[J]. IEEE Trans on Automatic Control, 2001, 46 (8): 1313 -1318.

DOI: 10.1109/9.940941

Google Scholar

[5] Hu H, Woo P Y. Fuzzy supervisory sliding-mode and neural- network control for robotic manipulators [J]. IEEE Trans. on Electron, 2006, 53(3): 929-940.

DOI: 10.1109/tie.2006.874261

Google Scholar

[6] Qian Xuesen, Song Jian. Engineering control theory [M]. Beijing: science press, (1983).

Google Scholar

[7] He Chengbo Liu Kaipei, Wei Hongjie, Zhang Chongjun. Based on internal model control strategy of self-tuning PID controller [J] journal of wuhan university (engineering science), 2002, 35 (2) : 108-112.

Google Scholar

[8] Gao Jian, Huang Xinhan, Peng Gang, Yang Qiyu, Yang Tao. Mobile robot motion control based on Fuzzy - PID control engineering [J], Control Engineering, 2004, 11 (6) : 525-528.

DOI: 10.1109/wcica.2006.1713765

Google Scholar

[9] Zhao Jun, Chen Jianjun. An uncertain object adaptive intelligent PID control system [J]. Journal of instruments and meters, 2008, 29 (6) : 1193-1198.

Google Scholar

[10] Liu Jinkun. Robot control system design and Matlab simulation [M]. Qinghua university press, Beijing. (2008).

Google Scholar

[11] Watanabe K, Tang J, Nakamura M, et al. A Fuzzy-Gaussian Neural Network and Its Application to Mobile Robot Control[J]. IEEE Transactions on Control Systems Technology, 1996, 4(2): 193-199.

DOI: 10.1109/87.486346

Google Scholar