Fuzzy decision based sliding mode robust adaptive control for bulldozer

被引：4

作者：

Bai, Han ^{[1
]}

Guan, Cheng ^{[1
]}

Pan, Shuang-Xia ^{[1
]}

机构：

[1] College of Mechanical and Energy Engineering, Zhejiang University

来源：

Zhejiang Daxue Xuebao(Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2009年 / 43卷 / 12期

关键词：

Bulldozer automatic control; Electro-hydraulic system; Fuzzy decision; Robust adaptive control; Sliding mode control;

D O I：

10.3785/j.issn.1008-973X.2009.12.009

中图分类号：

学科分类号：

摘要：

An automatic control strategy was proposed for bulldozer working device to improve working efficiency. The bulldozer blade position was optimized by fuzzy decision combined with human experience and experiments according to engine speed and its change rate. A sliding mode robust adaptive controller was designed to track the fuzzy decision values considering the characteristics of electro-hydraulic servo system. The recursive technique and state feedback linearization scheme were used to obtain a sliding mode controller. The system parameter update law was presented and a robust adaptive controller combined with sliding mode method was designed to accurately track fuzzy decision values based on Lyapunov stability theory. Experimental results show that the sliding mode robust adaptive controller has good robustness and remarkably improves the tracking accuracy. Fuzzy decision reasonably optimizes the desired blade position and improves the bulldozer working efficiency.

引用

页码：2178 / 2185

页数：7

共 18 条

[1]

Zhang Q., Feng P.-E., Application of PID control technique based on parameter fuzzy self-modify in dozer control system, Control Theory and Applications, 14, 2, pp. 287-291, (1997)

[2]

Terano T., Masui S., Nagaya K., Experimental study of fuzzy control for bulldozer, Proceedings of 1992 IEEE Region 10 International Conference, pp. 644-647, (1992)

[3]

Wang S.-M., CMAC neural network control for bulldozer working device, Transactions of the Chinese Society for Agricultural Machinery, 36, 2, pp. 140-142, (2005)

[4]

Stoev J., Choi J.Y., Farrell J., Adaptive control for output feedback nonlinear systems in the presence of modeling errors, Automatica, 38, 8, pp. 1761-1767, (2001)

[5]

Zhou J., Zhang C., Wen C., Robust adaptive output control of uncertain nonlinear plants with unknown backlash nonlinearity, IEEE Transactions on Automatic Control, 52, 3, pp. 503-509, (2007)

[6]

Zhu X.-L., Zhang X.-D., Ding Z.-Z., Et al., Adaptive nonlinear PCA algorithms for blind source separation without prewhitening, IEEE Transactions on Circuits and Systems-I: Regular Papers, 53, 3, pp. 143-149, (2006)

[7]

Lin F.J., Teng L.T., Shieh P.H., Intelligent adaptive backstepping control system for magnetic levitation apparatus, IEEE Transactions on Magnetics, 43, 5, pp. 2009-2018, (2007)

[8]

Liu H., Costa R.R., Lizarralde F., Lyapunov/Passivity based adaptive control of relative degree two MIMO systems with an application to visual servoing, IEEE Transactions on Automatic Control, 52, 2, pp. 364-371, (2007)

[9]

Guan C., Zhu S.-A., Derivative and integral sliding mode adaptive control for a class of nonlinear system and its application to an electro-hydraulic servo system, Proceedings of the CSEE, 25, 4, pp. 103-108, (2005)

[10]

Kino H., Yahiro T., Takemura F., Et al., Robust PD control using adaptive compensation for completely restrained parallel-wire driven robots: Translational systems using the minimum number of wires under zero-gravity condition, IEEE Transactions on Robotics, 23, 4, pp. 803-812, (2007)

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