Global approximation based adaptive RBF neural network control for supercavitating vehicles

doi:10.21629/JSEE.2018.04.14

Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (4): 797-804.doi: 10.21629/JSEE.2018.04.14

• Control Theory and Application • Previous Articles Next Articles

Global approximation based adaptive RBF neural network control for supercavitating vehicles

Yang LI(), Mingyong LIU*(), Xiaojian ZHANG(), Xingguang PENG()

School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China

Received:2017-05-18 Online:2018-08-01 Published:2018-08-30
Contact: Mingyong LIU E-mail:liyang_116@yeah.net;liumingyong@nwpu.edu.cn;xiaojiandr@outlook.com;pxg@nwpu.edu.cn
About author:LI Yang was born in 1987. He is a Ph.D. candidate in School of Marine Science and Technology, Northwestern Polytechnical University. His research interests are navigation and control of underwater super cavitation vehicle. E-mail: liyang_116@yeah.net|LIU Mingyong was born in 1971. He is a professor in School of Marine Science and Technology, Northwestern Polytechnical University. His research interests are navigation guidance and control of underwater vehicle. E-mail: liumingyong@nwpu.edu.cn|ZHANG Xiaojian was born in 1985. He is a Ph.D. candidate in School of Marine Science and Technology, Northwestern Polytechnical University. His research interests are guidance and control of underwater vehicle. E-mail: xiaojiandr@outlook.com|PENG Xingguang was born in 1982. He is an associate professor in School of Marine Science and Technology, Northwestern Polytechnical University. His research interests are path planning and cooperative co-evolutionary algorithm. E-mail: pxg@nwpu.edu.cn
Supported by:
the National Natural Science Foundation of China(51679201);the National Natural Science Foundation of China(61473233);This work was supported by the National Natural Science Foundation of China (51679201; 61473233)

Abstract

Abstract:

A global approximation based adaptive radial basis function (RBF) neural network control strategy is proposed for the trajectory tracking control of supercavitating vehicles (SV). A nominal model is built firstly with the unknown disturbance. Next, the control scheme is established consisting of a computed torque controller (CTC) for the practical vehicle and an RBF neural network controller to estimate model error between the practical vehicle and the nominal model. The network weights are adapted by employing a Lyapunov-based design. Then it is shown by the Lyapunov theory that the trajectory tracking errors asymptotically converge to a small neighborhood of zero. The control performance of the proposed controller is illustrated by simulation.

Key words: radial basis function (RBF) neural network, computed torque controller (CTC), adaptive control, supercavitating vehicle (SV)

Yang LI, Mingyong LIU, Xiaojian ZHANG, Xingguang PENG. Global approximation based adaptive RBF neural network control for supercavitating vehicles[J]. Journal of Systems Engineering and Electronics, 2018, 29(4): 797-804.

Figures/Tables 11

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References 24

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Parameter	Description	Value
$g$/(m/s$^{2}$)	Gravitational acceleration	9.81
$m$/kg	Mass	22
$r$/m	Cavitator radius	0.019 1
$R$/m	Vehicle radius	0.050 8
$L$/m	Vehicle length	1.8
$\sigma $	Cavitation number	0.03
$V$/(m/s)	Axial velocity	50
$\rho /$(kg/m$^3$)	Density of fluid	1 000

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Global approximation based adaptive RBF neural network control for supercavitating vehicles

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