Distributed Adaptive Neural Network Constraint Containment Control for the Benthic Autonomous Underwater Vehicles

2021 ◽  
Author(s):  
Yanchao Sun ◽  
Yutong Du ◽  
Hongde Qin
Keyword(s):  
Neural Network ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Adaptive Neural Network ◽  
Containment Control ◽  
Network Constraint

scholarly journals Spatial Path Following for AUVs Using Adaptive Neural Network Controllers

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Jiajia Zhou ◽  
Zhaodong Tang ◽  
Honghan Zhang ◽  
Jianfang Jiao
Keyword(s):  
Neural Network ◽  
Autonomous Underwater Vehicles ◽  
Path Following ◽  
Underwater Vehicles ◽  
Ocean Current ◽  
Lyapunov Stability Theorem ◽  
Adaptive Neural Network ◽  
Path Following Control ◽  
Following Error ◽  
Neural Network Controllers

The spatial path following control problem of autonomous underwater vehicles (AUVs) is addressed in this paper. In order to realize AUVs’ spatial path following control under systemic variations and ocean current, three adaptive neural network controllers which are based on the Lyapunov stability theorem are introduced to estimate uncertain parameters of the vehicle’s model and unknown current disturbances. These controllers are designed to guarantee that all the error states in the path following system are asymptotically stable. Simulation results demonstrated that the proposed controller was effective in reducing the path following error and was robust against the disturbances caused by vehicle's uncertainty and ocean currents.

scholarly journals Publish and Subscribe-Based Formation and Containment Control of Heterogeneous Robotic System with Actuator Time Delay

2021 ◽  
Vol 11 (19) ◽  
pp. 9145
Author(s):  
Siddig M. Elkhider ◽  
Omar Al-Buraiki ◽  
Sami El-Ferik
Keyword(s):  
Time Delay ◽  
Obstacle Avoidance ◽  
Data Transmission ◽  
Data Exchange ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Containment Control ◽  
L1 Adaptive Control ◽  
Navigation Algorithm ◽  
Local Controller

This paper addresses the problem of controlling a heterogeneous system composed of multiple Unmanned Aerial Vehicles (UAVs) and Autonomous Underwater Vehicles (AUVs) for formation and containment maintenance. The proposed approach considers actuator time delay and, in addition to formation and containment, considers obstacle avoidance, and offers a robust navigation algorithm and uses a reliable middleware for data transmission and exchange. The methodology followed uses both flocking technique and modified L1 adaptive control to ensure the proper navigation and coordination while avoiding obstacles. The data exchange between all the agents is provided through the data distribution services (DDS) middleware, which solves the interoperability issue when dealing with heterogeneous multiagent systems. The modified L1 controller is a local controller for stabilizing the dynamic model of each UAV and AUV, and the flocking approach is used to coordinate the followers around the leader or within the space delimited by their leaders. Potential Field (PF) allows obstacle avoidance during the agents’ movement. The performance of the proposed approach under the considerations mentioned above are verified and demonstrated using simulations.

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