scholarly journals Robust Adaptive Self-Structuring Neural Network Bounded Target Tracking Control of Underactuated Surface Vessels

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Haitao Liu ◽  
Jianfei Lin ◽  
Guoyan Yu ◽  
Jianbin Yuan
Keyword(s):  
Neural Network ◽  
Target Tracking ◽  
Input Saturation ◽  
Approximate Model ◽  
Model Uncertainties ◽  
The Neural Network ◽  
Surface Vessels ◽  
Underactuated Surface Vessels ◽  
Unknown Disturbances ◽  
Robust Adaptive

This paper studies the target-tracking problem of underactuated surface vessels with model uncertainties and external unknown disturbances. A composite robust adaptive self-structuring neural-network-bounded controller is proposed to improve system performance and avoid input saturation. An extended state observer is proposed to estimate the uncertain nonlinear term, including the unknown velocity of the tracking target, when only the measurement values of the line-of-sight range and angle can be obtained. An adaptive self-structuring neural network is developed to approximate model uncertainties and external unknown disturbances, which can effectively optimize the structure of the neural network to reduce the computational burden by adjusting the number of neurons online. The input-to-state stability of the total closed-loop system is analyzed by the cascade stability theorem. The simulation results verify the effectiveness of the proposed method.

Adaptive neural formation control of autonomous underactuated surface vessels based on disturbance observer with leader–follower strategy

2021 ◽  
pp. 014233122110224
Author(s):  
Chunqiang Wu ◽  
Meijiao Zhao ◽  
Cheng Min ◽  
Yueying Wang ◽  
Jun Luo
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Disturbance Observer ◽  
Formation Control ◽  
Time Varying ◽  
Adaptive Neural Network ◽  
Model Uncertainties ◽  
Dynamic Surface ◽  
Surface Vessels ◽  
Underactuated Surface Vessels

In this paper, a leader–follower formation control strategy is presented based on adaptive neural network and disturbance observer, which is aimed at resolving model uncertainties as well as the time-varying disturbances for autonomous underactuated surface vessels. The model uncertainties which can be expressed by unknown nonlinear functions are approximated and compensated by the adaptive neural network. The disturbance observer introduced can estimate time-varying disturbances and compensate them to the feedforward control loop, so as to make the external time-varying disturbances suppressed and the robustness of controller against the disturbances improved. The dynamic surface control technology is applied in the procedure of designing the controller through utilizing the backstepping method, which solves the computational explosion of the derivative of virtual control signals. Finally, through Lyapunov analysis, the stability of adaptive neural formation control system is proved and all the error signals uniformly converge to a very small range ultimately. The excellent performance of the presented formation control strategy is demonstrated through numerical simulations.

scholarly journals Distributed Tracking Control for Connectivity-Preserving and Collision-Avoiding Formation Tracking of Underactuated Surface Vessels with Input Saturation

2020 ◽  
Vol 10 (10) ◽  
pp. 3372
Author(s):  
Guoqing Xia ◽  
Xiaoming Xia ◽  
Bo Zhao ◽  
Chuang Sun ◽  
Xianxin Sun
Keyword(s):  
Collision Avoidance ◽  
Tracking Control ◽  
Input Saturation ◽  
Model Uncertainties ◽  
Environmental Disturbances ◽  
Formation Tracking ◽  
Surface Vessels ◽  
Underactuated Surface Vessels ◽  
Connectivity Preservation ◽  
The Stability

This paper investigates the formation tracking control problem of a group of underactuated surface vessels (USVs) in the presence of model uncertainties and environmental disturbances. Additional constraints, such as collision avoidance, heterogeneous limited communication range and input saturation are also considered. A modified barrier Lyapunov function (BLF) is introduced to achieve the connectivity preservation, the collision avoidance and the distributed formation tracking. Extended state observer (ESO) is employed to estimate total disturbances consisting of environmental disturbances and model uncertainties. Auxiliary variables are introduced to deal with the underactuated problem and input saturation. A distributed controller is developed for each USV. Using the Lyapunov method analyze the stability of the system, it is proven that all signals are bounded and tracking errors converge to a neighborhood of the origin. Simulation results show that the proposed controller is practicable and effective.

Adaptive fuzzy tracking control for underactuated surface vessels with unmodeled dynamics and input saturation

2020 ◽  
Vol 103 ◽  
pp. 52-62
Author(s):  
Yingjie Deng ◽  
Xianku Zhang ◽  
Namkyun Im ◽  
Guoqing Zhang ◽  
Qiang Zhang
Keyword(s):  
Tracking Control ◽  
Input Saturation ◽  
Unmodeled Dynamics ◽  
Adaptive Fuzzy ◽  
Surface Vessels ◽  
Underactuated Surface Vessels

scholarly journals Neural networks-based robust adaptive flight path tracking control of large transport

2018 ◽  
Vol 38 (3) ◽  
pp. 268-278
Author(s):  
Maolong Lv ◽  
Xiuxia Sun ◽  
G. Z. Xu ◽  
Z. T. Wang
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Nonlinear Modeling ◽  
Dynamic Surface Control ◽  
Continuous Functions ◽  
Network Dynamic ◽  
Dynamic Surface ◽  
The Neural Network ◽  
Neural Network Dynamic ◽  
Robust Adaptive

For the ultralow altitude airdrop decline stage, many factors such as actuator nonlinearity, the uncertain atmospheric disturbances, and model unknown nonlinearity affect the precision of trajectory tracking. A robust adaptive neural network dynamic surface control method is proposed. The neural network is used to approximate unknown nonlinear continuous functions of the model, and a nonlinear robust term is introduced to eliminate the actuator’s nonlinear modeling error and external disturbances. From Lyapunov stability theorem, it is rigorously proved that all the signals in the closed-loop system are bounded. Simulation results confirm the perfect tracking performance and strong robustness of the proposed method.

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