Predictive S Control of AUV Based on Model of Support Vector Machine

Abstract:

Because of nonlinear dynamic performances and uncertain working environment, precise motion control of Automatic Underwater Vehicle (AUV) has always been a problem. For the time lag and nonlinearity of AUV, a new method called Predictive S Control (PSC) based on model of Support Vector Machine (SVM) is presented. Firstly, in establishing the model of SVM, the fine property of approaching to the nonlinear model is utilized to solve the predictive problem. Then, the control parameters of S controller are optimized by constructing the error function. At last the motion control of AUV is realized. It has been proved that this method is feasible and effective. Using this method, a good control result is obtained in the simulation.

• Periodical: Advanced Materials Research (Volume 340)
• Pages: 421-428
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.340.421
• Online since: September 2011
• Authors: Guo Cheng Zhang, Lei Zhang, Lei Wan, Ji Qing Li
• Keywords: Automatic Underwater Vehicle (AUV), Motion Control, Predictive S Control (PSC), Support Vector Machine (SVM)

References

[1] W. Wang, The theory and application of generalized predictive control, Beijing: Publishing House of Science, (1998).

[2] X.Y. Huang and X.F. Zhu, Research status and expectation of predictive control, Oil industry and Automatization, vol. 2, pp.27-31, (2005).

[3] Yuh. J, Modeling and control of underwater robotic vehicles, IEEE Trans. Sys. Man and Cyber, p.1475–1483, (1990).

DOI: 10.1109/21.61218

[4] Yuh. J, Design and Control of Autonomous Underwater Robots, Autonomous Robots, pp.7-24, August (2000).

[5] Yuh. J, An adaptive and learning control system for underwater robots, 13th World Congress International Federation of Automatic Control, San Francisco, CA, p.145–150, (1996).

[6] S.D. Shi, Manipulation of submarine, Beijing: Publishing House of National defence industry, (1995).

[7] J.C. Liu, H.N. Yu and Y.R. Xu, Improved S Plane Control Algorithm for Underwater Vehicles [J], Journal of Harbin Engineering University, pp.33-36, February (2002).

[8] X.M. Liu and Y.R. Xu, S control of automatic underwater vehicles[J], Ocean Engineer, vol. 19, no. 3, pp, 21-24, (2001).

[9] VAPNIK.V. N, The Nature of Statistical Learning [M]. NY：Springer, 1999.

[10] VAPNIK V N，GOLOWICH S．E and SMOLA A J, Support vector method for function approximation regression estimation and signal processing[C], Neural Information Processing Systems, Cambridge：MIT Press, pp, 281—287, (1997).

[11] SUYKENS J A K, GESTELT V and BRABANTER J D, Least Support Vector Machines[M], Singapore：World Scientific Publishing Co，2002.

[12] H.R. Zhang , Z.Z. Han and C.G. Li, Identification of nonlinear system based on support vector machine, Paper of System Simulation, pp.119-121, (2003).