Research on the course control of USV based on improved ADRC

Hu, Junxiang; Ge, Yuan; Xu, Zhengwen; Liu, Shuo; Wu, JH

doi:10.1080/21642583.2020.1865216

Cited by 11 publications

(8 citation statements)

References 15 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For a problem that eventually stabilizes at the origin, based on Newton's second laws of motion, the motion state depends on x 1 +x 2 |x 2 |/(2β). Thus, its fast optimal control function can be concluded based on the literature [9]:…”

Section: A Tracking Differentiatormentioning

confidence: 99%

“…Thanks to its strongly autonomous navigation capability, environmental adaptability and modular design advantage, it has been widely used in hydrologic reconnaissance, maritime search and rescue, and navigation of formation and other fields [1][2][3][4][5][6]. To solve the nonlinear interference problem caused by the overlay and the interaction of multi interference, and the poor robustness of control methods due to the uncertainty of the model parameters [7][8], active disturbance rejection control [9][10] (ADRC), fuzzy control [11][12], backstepping control [13][14][15], sliding mode control [16][17][18][19][20][21] (SMC), and proportion integration differentiation (PID) control [22][23][24], combined with intelligent algorithms and artificial neural networks [25][26][27] (ANN), have been introduced by scholars.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adptive Heading Control of Underactuated Unmanned Surface Vehicle Based on Improved Backpropagation Neural Network

Dong

Liu³

et al. 2023

Polish Maritime Research

View full text Add to dashboard Cite

Aiming at the challenges to the accurate and stable heading control of underactuated unmanned surface vehicles arising from the nonlinear interference caused by the overlay and the interaction of multi interference, and also the uncertainties of model parameters, a heading control algorithm for an underactuated unmanned surface vehicle based on an improved backpropagation neural network is proposed. Based on applying optimization theory to realize that the underactuated unmanned surface vehicle tracks the desired yaw angle and maintains it, the improved momentum of weight is combined with an improved tracking differentiator to improve the robustness of the system and the dynamic property of the control. A hyperbolic tangent function is used to establish the nonlinear mappings an approximate method is adopted to summarize the general mathematical expressions, and the gradient descent method is applied to ensure the convergence. The simulation results show that the proposed algorithm has the advantages of strong robustness, strong anti-interference and high control accuracy. Compared with two commonly used heading control algorithms, the accuracy of the heading control in the complex environment of the proposed algorithm is improved by more than 50%.

show abstract

Section: A Tracking Differentiatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adptive Heading Control of Underactuated Unmanned Surface Vehicle Based on Improved Backpropagation Neural Network

Dong

Liu³

et al. 2023

Polish Maritime Research

View full text Add to dashboard Cite

show abstract

“…ADRC is composed of three parts [41]: TD, ESO and NLSEF. The structure of ADRC is shown in Figure 3 [42]. ADRC is a nonlinear control that does not rely on precise mathematical models [43].…”

Section: Structure Of Adrcmentioning

confidence: 99%

Ship Dynamic Positioning Control Based on Active Disturbance Rejection Control

Chen

Gao

et al. 2022

JMSE

View full text Add to dashboard Cite

Nowadays, Dynamic Positioning (DP) is applied to various tasks such as subsea pipeline laying and the requirements for the positioning performance in marine operations are higher and higher. The main objective of this paper is to design a DP controller based on the Active Disturbance Rejection Control (ADRC) to solve the problems of long response time, large overshoot and low positioning accuracy in ship positioning. Firstly, the mathematical models of the ship and environmental disturbances are established. Secondly, the basic principle of ADRC is described. Meanwhile, stability analysis of the control system is introduced. Thirdly, ADRC is improved by the fal function filter and phase prediction method which solve the problem of dither and phase delay in tracking differentiators. Finally, simulations are carried out to verify the performance of the designed ADRC and the improved ADRC. Several simulation results show that the designed ADRC can realize the fixed-point control of the ship, which effectively solves the problems of long response time, overshoot and positioning accuracy, and compared with the traditional ADRC, the improved ADRC can reduce the error of straight track control, which indicates that ADRC can meet the requirements for the positioning performance and has a strong application value.

show abstract

“…A special feedback mechanism is used to make the observed value follow the actual value. e complete algorithm of the ADRC is as follows [10,25].…”

Section: The Principle Of Adrcmentioning

confidence: 99%

Design of Active Disturbance Rejection Controller for Dynamic Positioning Based on Improved Particle Swarm Optimization

Chen

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Intelligent control methods play an important role in the dynamic positioning system (DPS). To improve the control accuracy of dynamic positioning systems, an improved active disturbance rejection controller (IADRC) is designed in this study, which can optimize the steady-state performance of the system and improve the tracking accuracy of the system. For nonlinear active disturbance rejection controllers, their internal parameters are complex and numerous, with difficult settings. Proper parameters cannot be found with the trial and error method, and traditional optimization algorithms showcase some problems, such as slow convergence speed, leading to frequent failures in local optimal solutions. An optimized particle swarm optimization (IPSO) algorithm is applied to the IADRC parameter setting to boost the global search ability and the local development function. Simulation analyses demonstrate that, compared with other intelligent control methods, the IPSO-based IADRC dynamic positioning system has advantages such as fast response speed and strong anti-interference ability.

show abstract

Research on the course control of USV based on improved ADRC

Cited by 11 publications

References 15 publications

Adptive Heading Control of Underactuated Unmanned Surface Vehicle Based on Improved Backpropagation Neural Network

Adptive Heading Control of Underactuated Unmanned Surface Vehicle Based on Improved Backpropagation Neural Network

Ship Dynamic Positioning Control Based on Active Disturbance Rejection Control

Design of Active Disturbance Rejection Controller for Dynamic Positioning Based on Improved Particle Swarm Optimization

Contact Info

Product

Resources

About