ARTÍCULO
TITULO

Robust Adaptive Depth Control of Hybrid Underwater Glider in Vertical Plane

Ngoc-Duc Nguyen    
Hyeung-Sik Choi    
Han-Sol Jin    
Jiafeng Huang    
Jae-Heon Lee    

Resumen

Hybrid underwater glider (HUG) is an advanced autonomous underwater vehicle with propellers capable of sustainable operations for many months. Under the underwater disturbances and parameter uncertainties, it is difficult that the HUG coordinates with the desired depth in a robust manner. In this study, a robust adaptive control algorithm for the HUG is proposed. In the descend and ascend periods, the pitch control is designed using backstepping technique and direct adaptive control. When the vehicle approaches the target depth, the surge speed control using adaptive control combined with the pitch control is used to keep the vehicle at the desired depth with a constant cruising speed in the presence of the disturbances. The stability of the proposed controller is verified by using the Lyapunov theorem. Finally, the computer simulation using the numerical method is conducted to show the effectiveness of the proposed controller for a hybrid underwater glider system.

 Artículos similares

       
 
Rong Li, Zhengliang Yang, Gaowei Yan, Long Jian, Guoqiang Li and Zhiqiang Li    
This paper uses the adaptive dynamic programming (ADP) method to achieve optimal trajectory tracking control for quadrotors. Relying on an established mathematical model of a quadrotor, the approximate optimal trajectory tracking control, which consists ... ver más
Revista: Aerospace

 
Juan Luis Pérez-Ruiz, Yu Tang, Igor Loboda and Luis Angel Miró-Zárate    
In the field of aircraft engine diagnostics, many advanced algorithms have been proposed over the last few years. However, there is still wide room for improvement, especially in the development of more integrated and complete engine health management sy... ver más
Revista: Aerospace

 
Ryota Higashimoto, Soh Yoshida and Mitsuji Muneyasu    
This paper addresses the performance degradation of deep neural networks caused by learning with noisy labels. Recent research on this topic has exploited the memorization effect: networks fit data with clean labels during the early stages of learning an... ver más
Revista: Applied Sciences

 
Woonghee Lee, Mingeon Ju, Yura Sim, Young Kul Jung, Tae Hyung Kim and Younghoon Kim    
Deep learning-based segmentation models have made a profound impact on medical procedures, with U-Net based computed tomography (CT) segmentation models exhibiting remarkable performance. Yet, even with these advances, these models are found to be vulner... ver más
Revista: Applied Sciences

 
Haohao Guo, Tianxiang Xiang, Yancheng Liu, Qiaofen Zhang, Yi Wei and Fengkui Zhang    
This paper proposes a new method for compensating current measurement errors in shipboard permanent magnet propulsion motors. The method utilizes cascade decoupling second-order generalized integrators (SOGIs) and adaptive linear neurons (ADALINEs) as th... ver más