Adaptive Fuzzy Tracking Control of Unmanned Surface Vehicle with State and Input Quantization

doi:10.12141/j.issn.1000-565X.230215

Abstract

Abstract:

An adaptive feedback tracking control scheme with state and input quantization was designed for the track tracking control problem of unmanned unmanned surface vehicle under the restricted communication bandwidth at sea. While ensuring effective tracking, it reduces the burden of maritime communication signal transmission, decreases the actuator execution frequency and reduces the control amplitude. Firstly, the system control law was designed based on the adaptive backstepping method, which combined with the dynamic surface technology to effectively reduce the computational inflation problem of the virtual control law. For the uncertain terms existing in the control system, a fuzzy logic system was used for approximation. Next, the state variables and input variables in the control system were quantized separately using a uniform quantizer, and the quantized state feedback information was used in the design of the unmanned surface vehicle track tracking controller. Based on the obtained quantization information, a control law for tracking the trajectory of an unmanned surface vehicle was proposed under the conditions of simultaneous consideration of state and input quantization. The boundedness of the errors between quantized and unquantized variables in the closed-loop control system was demonstrated by a recursive approach. The stability of the designed fuzzy adaptive feedback tracking control system with state quantization and input quantization was demonstrated based on Lyapunov stability theory when both state quantization and input quantization were considered. Finally, the effectiveness of the proposed scheme is verified by two sets of simulation experiments. That is, under the simultaneous consideration of state quantification and input quantification, the unmanned surface vehicle can still maintain a good tracking performance of the ideal trajectory, and effectively reduce the execution frequency of the actuator, which is more in line with the practice of navigation engineering.

Key words: unmanned surface vehicle, trajectory tracking control, state and input quantization, fuzzy adaptive control, quantization error

CLC Number:

TP273

NING Jun, MA Yifan, LI Wei, et al. Adaptive Fuzzy Tracking Control of Unmanned Surface Vehicle with State and Input Quantization[J]. Journal of South China University of Technology(Natural Science Edition), 2024, 52(5): 52-61.

Figures/Tables 9

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