A Searching Technique for Obstacle-Avoidance of Autonomous Underwater Vehicles by Using the Self-Tuning Fuzzy Controller

2014 ◽  
Author(s):  
Yu Hsien Lin ◽  
Ming Chung Fang ◽  
Hui Hua Chang
Keyword(s):  
Obstacle Avoidance ◽  
Fuzzy Controller ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
The Self ◽  
Fuzzy Relations ◽  
Image Detection ◽  
Underwater Image ◽  
Planar Motion Mechanism ◽  
Self Tuning

This study develops a heuristic searching technique for obstacle-avoidance of autonomous underwater vehicles (AUVs) in varying ocean environments by using the self-tuning fuzzy controller. The corresponding hydrodynamic coefficients for the AUV are obtained by the test of Planar Motion Mechanism (PMM), which serves as the important data inputs for the control system. Subsequently, the self-tuning fuzzy controller would be adopted to command the propulsion of AUVs. The function of obstacle-avoidance is based on the underwater image detection method with the BK triangle sub-product of fuzzy relations which can evaluate the safety and remoteness of the candidate routes and the successive optimal strategic routing can then be selected. In the present simulations, the current effect is used to investigate the maneuvering performance of obstacle-avoidance. Eventually, the present study indicates that the self-tuning fuzzy controller, combined with the image detection technique based on BK triangle sub-product of fuzzy relations, is verified to be a useful searching technique for obstacle-avoidance of AUVs in depth variation.

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.

A Novel Classified Self-Organising Map Applied to Task Assignment

2020 ◽  
Vol 73 (5) ◽  
pp. 1129-1145
Author(s):  
Yun Qu ◽  
Daqi Zhu
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Task Assignment ◽  
Underwater Vehicles ◽  
The Self ◽  
Sensor Nodes ◽  
Sensor Technology ◽  
The Novel ◽  
Total Energy Consumption ◽  
Map Algorithm ◽  
Self Organising Map

With the development of sensor technology, sensor nodes are increasingly being used in underwater environments. The strategy presented in this paper is designed to solve the problem of using a limited number of autonomous underwater vehicles (AUVs) to complete tasks such as data collection from sensor nodes when the number of AUVs is less than the number of target sensors. A novel classified self-organising map algorithm is proposed to solve the problem. First, according to the K-means algorithm, targets are classified into groups that are determined by the number of AUVs. Second, according to the self-organising map algorithm, AUVs are matched with groups. Third, each AUV is provided with the accessible order of the targets in the group. The novel classified self-organising map algorithm can be used not only to reduce the total energy consumption in a multi-AUV system, but also to give the most efficient accessible order of targets for AUVs. Results of simulations conducted to prove the applicability of the algorithm are given.

A study on water level control of PWR steam generator at low power and the self-tuning of its fuzzy controller

1995 ◽  
Vol 74 (1) ◽  
pp. 43-51 ◽  
Author(s):  
Nanju Na ◽  
Keechoon Kwon ◽  
Changshik Ham ◽  
Zeungnam Bien
Keyword(s):  
Low Power ◽  
Water Level ◽  
Steam Generator ◽  
Fuzzy Controller ◽  
The Self ◽  
Level Control ◽  
Self Tuning

Positioning System with Progressive Wave-Type Ultrasonic Motor under Self-Tuning Fuzzy Control

1995 ◽  
Vol 7 (1) ◽  
pp. 63-68 ◽  
Author(s):  
Junji Fukumi ◽  
◽  
Takuya Kamano ◽  
Takayuki Suzuki ◽  
Yu Kataoka ◽  
...  
Keyword(s):  
Feedback Loop ◽  
Fuzzy Controller ◽  
Lower Layer ◽  
Tracking Error ◽  
Ultrasonic Motor ◽  
The Self ◽  
Positioning System ◽  
Wave Type ◽  
Nonlinear Characteristics ◽  
Self Tuning

This paper considers the use of a self-tuning fuzzy controller for a positioning system with a progressive wavetype ultrasonic motor. The system consists of a feedback loop with a conventional controller and a self tuning fuzzy controller. The objective of the self tuning fuzzy controller is to restrain the adverse effect of nonlinear characteristics of the motor and to improve the tracking performance. The self-tuning fuzzy controller is functionally divided into two layers. The fuzzy rules are automatically adjusted by a tuning algorithm so that the tracking error is minimized in the upper layer. In lower layer, the output signal of the self tuning fuzzy controller is obtained by fuzzy reasoning procedure. After the tuning process is completed, the tracking error almost converges to zero, and the ultrasonic motor is no longer controlled by the fixed gain feedback controller but by the self-tuning fuzzy controller. The effectiveness of the proposed self-tuning fuzzy controller is demonstrated by an experiment.

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