Robust time-varying formation control for underactuated autonomous underwater vehicles with disturbances under input saturation

2019 ◽  
Vol 179 ◽  
pp. 180-188 ◽  
Author(s):  
Jian Li ◽  
Jialu Du ◽  
Wen-Jer Chang
2021 ◽  
Author(s):  
Yongnan Jia ◽  
Weicun Zhang

Abstract Due to the limitation of complexity and uncertainty of the underwater environment, the related technologies of autonomous underwater vehicles(AUVs) develop slowly. Therefore, an ingenious solution characterized by low cost, convenient operation, and low individual intelligence is urgently required. Inspired from these collective behaviours of gregarious creatures in nature, the coordination control problem of multiple AUVs is endowed with new research significance to complete complex underwater operational tasks. This paper aims to propose a general control scheme to solve the time-varying formation control problem of multiple AUVs that take into account the communication time delay. Firstly, a complete six-degrees-of-freedom dynamical model is applied instead of the real AUVs in the following theoretical analysis and simulation verification. Then, a metric-based nearest neighbour interacted rule is introduced to build the communication network of the system. Periodic sampling technology and zero-order hold loop are adopted to simplify the communication problem of time delay. Based on the above dynamical model and communication mechanism, a distributed collective control protocol is proposed to enable these AUVs asymptotically converge to a desired geometrical configuration on the condition that the initial communication network is undirected and connected. During the evolutionary process, no collision happens between any two AUVs. The formation configuration can be maintained until a simple switching controller works for the configuration transformation tasks. Finally, the simulation results proved the effectiveness of the above collective control scheme and visually exhibited the three-dimensional dynamical evolutionary process.


2011 ◽  
Vol 467-469 ◽  
pp. 1377-1385 ◽  
Author(s):  
Ming Zhong Yan ◽  
Da Qi Zhu

Complete coverage path planning (CCPP) is an essential issue for Autonomous Underwater Vehicles’ (AUV) tasks, such as submarine search operations and complete coverage ocean explorations. A CCPP approach based on biologically inspired neural network is proposed for AUVs in the context of completely unknown environment. The AUV path is autonomously planned without any prior knowledge of the time-varying workspace, without explicitly optimizing any global cost functions, and without any learning procedures. The simulation studies show that the proposed approaches are capable of planning more reasonable collision-free complete coverage paths in unknown underwater environment.


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