Method for classifying untyped objects based on cascade neural network filter and finite deterministic automaton

With the continuous progress of machine vision technology, crack detection in pipelines has been greatly improved. For crack detection in deep holes, inner tubes, and other environments, it is not only necessary to detect the existence of cracks, but also to collect important information regarding the crack detection direction for further analysis. Because shooting with a frontal field of view causes the real side wall images to produce certain distortions, the detection and calibration of cracks requires a certain amount of professional technology and time. It usually takes a long time to collect the image to eliminate the distortion, and then to identify the crack and mark the direction according to the data line. Therefore, a simple and efficient end-to-end neural network model for crack recognition and three-dimensional visualization are proposed by using a cascade network and simple recognition technology in conjunction with inertial navigation equipment. In addition, we screen the crack data via pixel calibration and eliminate the ambiguous data to make the visualization more accurate. Experiments in pipelines and burrows show that the accuracy, performance, and efficiency of the proposed method reached a high level.

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PD Control Compensation Based on a Cascade Neural Network Applied to a Robot Manipulator

Frontiers in Neurorobotics ◽

10.3389/fnbot.2020.577749 ◽

2020 ◽

Vol 14 ◽

Author(s):

Luis Arturo Soriano ◽

Erik Zamora ◽

J. M. Vazquez-Nicolas ◽

Gerardo Hernández ◽

José Antonio Barraza Madrigal ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Robot Manipulator ◽

Industrial Robot ◽

Identification Problem ◽

Adaptive Controller ◽

Pd Control ◽

Non Linear ◽

Cascade Neural Network ◽

Integral Gain

A Proportional Integral Derivative (PID) controller is commonly used to carry out tasks like position tracking in the industrial robot manipulator controller; however, over time, the PID integral gain generates degradation within the controller, which then produces reduced stability and bandwidth. A proportional derivative (PD) controller has been proposed to deal with the increase in integral gain but is limited if gravity is not compensated for. In practice, the dynamic system non-linearities frequently are unknown or hard to obtain. Adaptive controllers are online schemes that are used to deal with systems that present non-linear and uncertainties dynamics. Adaptive controller use measured data of system trajectory in order to learn and compensate the uncertainties and external disturbances. However, these techniques can adopt more efficient learning methods in order to improve their performance. In this work, a nominal control law is used to achieve a sub-optimal performance, and a scheme based on a cascade neural network is implemented to act as a non-linear compensation whose task is to improve upon the performance of the nominal controller. The main contributions of this work are neural compensation based on a cascade neural networks and the function to update the weights of neural network used. The algorithm is implemented using radial basis function neural networks and a recompense function that leads longer traces for an identification problem. A two-degree-of-freedom robot manipulator is proposed to validate the proposed scheme and compare it with conventional PD control compensation.

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