Design and test of an improved active disturbance rejection control system for water sampling unmanned surface vehicle

Wu, Defeng; Yuan, Kexin; Huang, Youqiang; Yuan, Zhiming; Hua, Lisha

doi:10.1016/j.oceaneng.2021.110367

Cited by 18 publications

(2 citation statements)

References 45 publications

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“…Due to the great practical value of unmanned surface vehicles (USVs) in scientific research, marine resource exploration and the military, the problem of motion control of USVs has received extensive attention. Trajectory tracking control, as an important component of USV motion control, plays a vital role in vehicle navigation [1,2]. However, due to the inherent complex dynamics of USVs, such as modeling uncertainties, the design of trajectory tracking controllers for USVs has always been a major challenge for researchers [3].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Control for Unmanned Surface Vehicles with Predefined-Time Tracking Performances

Tao

Yan

2023

JMSE

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This paper is concerned with the trajectory tracking control of unmanned surface vehicles (USVs) subject to input quantization, actuator faults and dead zones. In scenarios with dense marine facilities, there are constraints on the tracking performance and convergence time of USVs. First, the designed control signal is quantized by a hysteresis quantizer to reduce the transmission rate. Second, to guarantee the transient and steady-state tracking performance of the USV, a prescribed performance control technology with a predefined settling time is employed. Third, a predefined-time adaptive sliding mode control (SMC) method is designed by integrating the auxiliary function and the barrier function. Moreover, the lumped uncertainties caused by quantization, actuator faults, and dead zones are simultaneously processed using control gain based on barrier function. The proposed control method guarantees that the tracking error and sliding variable converge to the corresponding predefined bounds within a predefined time. The predefined bounds are independent of the upper bound on the lumped uncertainty. The stability of the controlled system is proven via the Lyapunov theorem. Finally, the effectiveness of the designed controller is verified by numerical simulations.

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Section: Introductionmentioning

confidence: 99%

Adaptive Sliding Mode Control for Unmanned Surface Vehicles with Predefined-Time Tracking Performances

Tao

Yan

2023

JMSE

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“…They conducted real-ship verification and experimentally demonstrated the operability improvement of the USV. Wu et al [15] proposed an improved active disturbance rejection control (ADRC) method and saturation function for the control system of the USV. The method enhances the anti-disturbance ability for addressing environmental disturbances caused by wind, waves and currents during water sampling.…”

Section: Introductionmentioning

confidence: 99%

Trajectory‐tracking control of an unmanned surface vehicle based on characteristic modelling approach: Implementation and field testing

Meng

Yang

2023

IET Cyber-Syst and Robotics

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In this study, a practical adaptive control scheme is proposed for the trajectory tracking of an unmanned surface vehicle via the characteristic modelling approach. Therefore, accurate tracking control can be achieved in the presence of unknown time-varying model parameters and environmental disturbances. The control scheme comprises a trajectory guidance module based on the virtual target approach and a tracking control module designed by characteristic modelling theory. Firstly, the ideal control commands of the yaw speed and surge speed are generated using the position errors between the vehicle and the virtual target. Then, a second-order characteristic model for the heading and surge speed channel is developed. The parameters of the model are updated by a real-time parameter identification algorithm. Based on this model, an integrated adaptive control law is designed which consists of golden-section control, feed-forward control and integral control. Finally, the development processes of the vehicle platform and the control algorithms are described, and the results of simulation and field experiments are presented and discussed.

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A method for detecting navigable areas in narrow rivers under complex reflection conditions

Zhang,

Hu,

et al. 2024

Engineering Reports

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The perception of unmanned surface vehicles is significantly influenced by the detection of navigable areas in narrow rivers. Conventional semantic segmentation networks are unable to resolve the numerous interferences on the water's surface, including highlights and inverted images. To solve this problem, a river surface image reflection removal generative adversarial network (RRGAN) is proposed to eliminate the interference of harsh water surface environment. The proposed RRGAN only uses a single generator to reduce the number of parameters. By adding AdaLIN layers in the generator to enhance the ability to generate low‐reflection images, the AdaLIN encoder (AdaLINE) is proposed to automatically generate normalized affine parameters. In addition, a cycle semantic consistency loss function with a single generator is proposed to ensure that the water region of the generated images remains unchanged. Finally, a two‐stage method for detecting navigable areas is proposed. In the first stage, the RRGAN is used to remove the interference on the water surface environment. In the second stage, the semantic segmentation network is used to segment the water body from the denoised image to determine the navigable areas on the water surface. The experimental results demonstrate that, in the complex and varied narrow river environment, the suggested RRGAN method can significantly reduce the reflection interference of the water surface and improve the accuracy of the water segmentation after the reflection is removed.

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Design and test of an improved active disturbance rejection control system for water sampling unmanned surface vehicle

Cited by 18 publications

References 45 publications

Adaptive Sliding Mode Control for Unmanned Surface Vehicles with Predefined-Time Tracking Performances

Adaptive Sliding Mode Control for Unmanned Surface Vehicles with Predefined-Time Tracking Performances

Trajectory‐tracking control of an unmanned surface vehicle based on characteristic modelling approach: Implementation and field testing

A method for detecting navigable areas in narrow rivers under complex reflection conditions

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