Finite-Time Observer Based Guidance and Control of Underactuated Surface Vehicles With Unknown Sideslip Angles and Disturbances

Wang, Weining; Sun, Zhuo; Yin, Jianchuan; Su, Shun‐Feng; Sharma, Sanjay

doi:10.1109/access.2018.2797084

Cited by 87 publications

(55 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unmanned surface vehicle (USV) plays a vital role in antisubmarine warfare, mine countermeasures, environmental detection, water sampling, personnel search and rescue in the ocean, and so on. [1][2][3] An effective course keeping controller is essential for the highly autonomous USV in navigation. 4 Because of the complex, nonlinear, and uncertain characteristics of the ship, 5 coupled with the complex and changeable external navigation environment such as wind, wave, and currents, the course keeping problem becomes more complicated.…”

Section: Introductionmentioning

confidence: 99%

Global fast terminal sliding mode control based on radial basis function neural network for course keeping of unmanned surface vehicle

Wan

Zhang

et al. 2019

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

A scheme to solve the course keeping problem of the unmanned surface vehicle with nonlinear and uncertain characteristics and unknown external disturbances is investigated in this article. The chattering existing in global fast terminal sliding mode controller in solving the course keeping problem of the unmanned surface vehicle with external disturbance is analyzed. To reduce the chattering and eliminate the influence of the unknown disturbance, an adaptive global fast terminal sliding mode controller based on radial basis function neural network is developed. The equivalent control that usually requires a precise model information of the system is computed using the radial basis function neural network. The weights of the neural network are online adjusted according to the adaptive law that is derived using Lyapunov method to ensure the stability of the closed-loop system. Using the online learning of the neural network, the nonlinear uncertainty of the system and the unknown disturbance of external environment are compensated, and the system chattering is reduced effectively as well. The simulation results demonstrate that the proposed controller can achieve a good performance regarding the fast response and smooth control.

show abstract

Section: Introductionmentioning

confidence: 99%

Global fast terminal sliding mode control based on radial basis function neural network for course keeping of unmanned surface vehicle

Wan

Zhang

et al. 2019

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

show abstract

“…Sliding mode control (SMC) [36,37,[60][61][62][63][64][65][66][67][68] has reliable robustness like other robust controllers [38][39][40][41][42] with ability of reduce the impacts of disturbance, so that it can be applied for a wide range of complex systems [43]. Moreover, it can be stably combined with complicated adaptive algorithms [44][45][46][47][48][49], and be easily integrated with other control techniques [50][51][52] in both mathematical theory and practical experiment. In addition, the sliding mode observer [23,24,[56][57][58][59][69][70][71][72] was suggested and developed for several decades, which brought about the operating safety and reliability of the exceedingly sophisticated and complex system.…”

Section: Introductionmentioning

confidence: 99%

Super‐Twisting Observer‐Based Integral Sliding Mode Control for Tracking the Rapid Acceleration of a Piston in a Hybrid Electro‐Hydraulic and Pneumatic System

Hoang

Lee

Dugarjav

2018

Asian Journal of Control

View full text Add to dashboard Cite

A new hybrid electro-hydraulic and pneumatic actuator system and its dynamic model for high-performance control are presented. This work focuses on tracking control of rapidly changing acceleration that is an advanced area with various practical applications in industries. The impact motion control of the actuator is one of challenging task due to the system instability during the transition state. Since composite disturbances derived from the inaccurate and unmodeled dynamics considerably reduce the control performance. A novel structure of variable integral sliding mode controls integrated with a sliding mode disturbance observer is proposed based on the super-twisting algorithm. With the control strategy, not only does the controller overcome the extreme sensitivity of the system during rapid movements, but it also eliminates the internal parameter uncertainties and external load disturbance while tracking rapid gain-scheduled acceleration. The results of the numerical simulation and field experiment are presented to assess the effectiveness of the proposed control scheme.

show abstract

“…Some research works focus on tracking control and navigation of an autonomous underwater vehicle (AUV). 3 Some research works on formation control of a group of AUVs. 4 In some other works, task assignment and path planning are the objects of their study.…”

Section: Introductionmentioning

confidence: 99%

Visual detection and feature recognition of underwater target using a novel model-based method

Chen

et al. 2018

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

Imaging is an important means to explore the ocean for underwater robotics. The diffuse attenuation coefficient of light in water is one of the most important optical properties of seawater. This article presents a model-based method to analyze the causes of distortion of underwater images. We built a platform for underwater image acquisition and target recognition. The model coefficients were calibrated with images captured underwater and in air. Experiments were carried out to verify the designed algorithm and the transmission error model. The experiments show that the presented method works well in improving the accuracy of feature extraction and recognition of underwater targets.

show abstract

Finite-Time Observer Based Guidance and Control of Underactuated Surface Vehicles With Unknown Sideslip Angles and Disturbances

Cited by 87 publications

References 45 publications

Global fast terminal sliding mode control based on radial basis function neural network for course keeping of unmanned surface vehicle

Global fast terminal sliding mode control based on radial basis function neural network for course keeping of unmanned surface vehicle

Super‐Twisting Observer‐Based Integral Sliding Mode Control for Tracking the Rapid Acceleration of a Piston in a Hybrid Electro‐Hydraulic and Pneumatic System

Visual detection and feature recognition of underwater target using a novel model-based method

Contact Info

Product

Resources

About