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Collision-free trajectory tracking strategy of a UUV via finite-time extended state observer-based sliding mode predictive control

delete2024-12-01
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PRE
AI
X
Xun Zhang
H
Huijun Chen
W
Wen Xing *
Z
Zhiguang Feng
H
Hai‐Long Jiang
DOI:10.1016/j.jfranklin.2024.107245delete
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Abstract

Abstract

En 中文
This paper focuses on a trajectory tracking problem for unmanned underwater vehicles (UUVs) subject to current disturbances and static obstacles. A double-loop framework is established. The kinematic governor employs model predictive control(MPC), which takes into account the UUV's kinematic characteristics as well as the conditions for obstacle avoidance when calculating the control command and provides an optimal velocity command input for the dynamic controller. Then, the dynamic controller is developed based on a fast finite-time extended state observer (FESO) and a fast adaptive integral terminal sliding mode controller (FAITSMC). The construction of the fast FESO integrates UUV's dynamics model, a proportional integral velocity variable, and a fractional order term of the output observation error, which can identify model uncertainties and external disturbances in finite time. By means of adaptive uncertainty compensation, the FAITSMC enables the error between actual speed and speed reference to converge to a minimum quickly. By applying Lyapunov stability theory and the finite-time analysis technique, sufficiency criteria are established to guide and keep the UUV on a reference trajectory via an inner-outer loop control structure. Finally, simulation examples in different scenarios are presented to verify the feasibility and effectiveness of the derived theoretical results.
Keywords:
Unmanned underwater vehicles
A double-loop architecture
Model predictive control
Sliding mode control
Finite-time extended state observer

Journal

J
Journal of the Franklin Institute-Engineering and Applied Mathematics
IF:
3.7
Papers:
6.2K
Citations:
1.5W

Organization

H
Harbin Engineering University
Scholars:
1.9W
Papers: 1.3W
Citations: 1.3W
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