Trajectory Tracking Control for Underactuated USV with Prescribed Performance and Input Quantization

This paper is devoted to the problem of prescribed performance trajectory tracking control for symmetrical underactuated unmanned surface vessels (USVs) in the presence of model uncertainties and input quantization. By combining backstepping filter mechanisms and adaptive algorithms, two robust cont...

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Bibliographic Details
Published inSymmetry (Basel) Vol. 13; no. 11; p. 2208
Main Authors Jiang, Kunyi, Mao, Lei, Su, Yumin, Zheng, Yuxin
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2021
Subjects
Adaptive algorithms
Adaptive control
Bandwidths
Communication
Control algorithms
Controllers
Data transmission
input quantization
Machine learning
Mathematical models
Measurement
minimum learning parameter
model-free control
Neural networks
Parameter identification
Parameters
prescribed performance control
Robust control
Tracking control
Trajectory control
underactuated USV
Yaw
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Summary:This paper is devoted to the problem of prescribed performance trajectory tracking control for symmetrical underactuated unmanned surface vessels (USVs) in the presence of model uncertainties and input quantization. By combining backstepping filter mechanisms and adaptive algorithms, two robust control architectures are investigated for surge motion and yaw motion. To guarantee the prespecified performance requirements for position tracking control, the constrained error dynamics are transformed to unconstrained ones by virtue of a tangent-type nonlinear mapping function. On the other hand, the inaccurate model can be identified through radial basis neural networks (RBFNNs), where the minimum learning parameter (MLP) algorithm is employed with a low computational complexity. Furthermore, quantization errors can be effectively reduced even when the parameters of the quantizer remain unavailable to designers. Finally, the effectiveness of the proposed controllers is verified via theoretical analyses and numerical simulations.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym13112208