Abstract
The present study investigates the position tracking control of the underactuated autonomous surface vehicle, which is subjected to parameters uncertainties and external disturbances. In this regard, the backstepping method, neural network, dynamic surface control and the sliding mode method are employed to design an adaptive robust controller. Moreover, a Lyapunov synthesis is utilized to verify the stability of the closed-loop control system. Following innovations are highlighted in this study: (i) The derivatives of the virtual control signals are obtained through the dynamic surface control, which overcomes the computational complexities of the conventional backstepping method. (ii) The designed controller can be easily applied in practical applications with no requirement to employ the neural network and state predictors to obtain model parameters. (iii) The prediction errors are combined with position tracking errors to construct the neural network updating laws, which improves the adaptation and the tracking performance. The simulation results demonstrate the effectiveness of the proposed position tracking controller.
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The authors acknowledge support by the National Natural Science Foundation of China (NSFC, Grant Nos. 11672094).
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Chengju Zhang was born in Puyang, Henan Province, China. He received the B.E. degree in Naval Architecture and Ocean Engineering from the School of Shipbuilding Engineering, Harbin Engineering University, Harbin, China, in 2017. He is now pursuing his Ph.D. degree in Astronautics at Harbin Institute of Technology. His current research interests include the motion control of marine surface vehicles.
Cong Wang received his Ph.D. degree in Mechanics from Harbin Institute of Technology, Harbin, China, in 2001. He received his Bachelor and Master degrees in Mechanical and Electrical Engineering from Northeast Forestry University in 1989 and 1993, respectively. He is currently a Professor at School of Astronautics, Harbin Institute of Technology, China. His current research interests include fluid mechanics and the motion control of underwater vehicles.
Yingjie Wei received his Ph.D. degree in Mechanics from Harbin Institute of Technology, Harbin, China, in 2003. He received his Bachelor and Master degrees in Oil and Gas Field Development from the Northeast Petroleum University in 1996 and 2000, respectively. He is currently a Professor at School of Astronautics, Harbin Institute of Technology, China. His current research interests include multiphase fluid mechanics and hydrodynamics of the underwater vehicles.
Jinqiang Wang was born Harbin, Heilongjiang Province, China. He received the B.E. degree in Naval Architecture and Ocean Engineering from the School of Shipbuilding Engineering, Harbin Engineering University, Harbin, China, in 2016. He is now pursuing his Ph.D. degree in Astronautics at Harbin Institute of Technology. His current research interests include guidance and the motion control of autonomous underwater vehicles.
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Zhang, C., Wang, C., Wei, Y. et al. Neural network adaptive position tracking control of underactuated autonomous surface vehicle. J Mech Sci Technol 34, 855–865 (2020). https://doi.org/10.1007/s12206-020-0135-2
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DOI: https://doi.org/10.1007/s12206-020-0135-2