Abstract

To improve the navigation ability of underwater tunnel monitoring robots at fixed distances, directions, depths, and heights and to improve the accuracy of tunnel monitoring, an adaptive control method for underwater tunnel monitoring robots based on the Internet of Things (IoT) and fuzzy neural network algorithms is proposed. The structure of underwater tunnel monitoring robots is analyzed based on the IoT, the convolutional neural network algorithm is used to extract the tracking target characteristics of the underwater tunnel monitoring robot, and the obstacle avoidance process of the underwater tunnel monitoring robot is analyzed. The membership degree of the input variable is calculated by the fuzzy control algorithm. The control rule optimizes the neural network algorithm, obtains the target characteristics displayed by the visual tracking of the underwater tunnel monitoring robot based on the fuzzy neural network, uses the adaptive control to estimate the optimal parameters, and finally obtains the adaptive sliding mode control of the underwater tunnel monitoring robot. The experimental results show that the proposed method can accurately realize the target tracking task of the underwater tunnel monitoring robot and has better obstacle avoidance ability.

Issue Section:
Technical Papers
Keywords:
fuzzy neural network algorithm, underwater tunnel monitoring robot, adaptive control, sliding mode control

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