Neural network-based target tracking control of underactuated autonomous underwater vehicles with a prescribed performance

2018 ◽  
Vol 167 ◽  
pp. 239-256 ◽  
Author(s):  
Omid Elhaki ◽  
Khoshnam Shojaei
Keyword(s):  
Neural Network ◽  
Target Tracking ◽  
Tracking Control ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Prescribed Performance

Neural Network-Based Tracking Control of Underactuated Autonomous Underwater Vehicles With Model Uncertainties

2014 ◽  
Vol 137 (2) ◽  
Author(s):  
Bong Seok Park
Keyword(s):  
Neural Network ◽  
Tracking Control ◽  
Control Method ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Reference Trajectory ◽  
Control Input ◽  
Model Uncertainties ◽  
Dynamic Surface ◽  
Hydrodynamic Damping

In this paper, we propose a neural network (NN)-based tracking control method for underactuated autonomous underwater vehicles (AUVs) with model uncertainties. In order to solve the difficulties in designing the controller for underactuated AUVs, the additional virtual control input is developed, and the approach angle, which generates the desired yaw angle to track any reference trajectory, is introduced. Moreover, the NNs are used to deal with model uncertainties in the hydrodynamic damping terms of AUVs. Finally, the proposed controller is designed based on the dynamic surface control (DSC) method, and the boundedness of all tracking errors is proved by using the Lyapunov stability theory. Some simulation results demonstrate the performance of the proposed control method.

Fixed-time sliding mode tracking control for autonomous underwater vehicles

2021 ◽  
Vol 117 ◽  
pp. 102928
Author(s):  
Jiaqi Zheng ◽  
Lei Song ◽  
Lingya Liu ◽  
Wenbin Yu ◽  
Yiyin Wang ◽  
...  
Keyword(s):  
Tracking Control ◽  
Sliding Mode ◽  
Autonomous Underwater Vehicles ◽  
Fixed Time ◽  
Underwater Vehicles ◽  
Mode Tracking
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