Adaptive Neural-PID Visual Servoing Tracking Control via Extreme Learning Machine

The vision-guided robot is intensively embedded in modern industry, but it is still a challenge to track moving objects in real time accurately. In this paper, a hybrid adaptive control scheme combined with an Extreme Learning Machine (ELM) and proportional–integral–derivative (PID) is proposed for...

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Bibliographic Details
Published in:Machines (Basel) 2022-09, Vol.10 (9), p.782
Main Authors: Luo, Junqi, Zhu, Liucun, Wu, Ning, Chen, Mingyou, Liu, Daopeng, Zhang, Zhenyu, Liu, Jiyuan
Format: Article
Language:English
Subjects:
Accuracy
Adaptive control
adaptive visual tracking
Algorithms
Artificial neural networks
Autoregressive moving average
Cameras
Control systems design
Controllers
ELM–PID control
Feature extraction
Kinematics
laser-camera system
Lasers
Machine learning
Machine vision
Management science
Masonry
Moving object recognition
Neural networks
Optical tracking
Optimal control
Parameters
Proportional integral derivative
Robotics
Robots
Servocontrol
Tracking control
Velocity
Vision
Visual control
visual servoing
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Summary:The vision-guided robot is intensively embedded in modern industry, but it is still a challenge to track moving objects in real time accurately. In this paper, a hybrid adaptive control scheme combined with an Extreme Learning Machine (ELM) and proportional–integral–derivative (PID) is proposed for dynamic visual tracking of the manipulator. The scheme extracts line features on the image plane based on a laser-camera system and determines an optimal control input to guide the robot, so that the image features are aligned with their desired positions. The observation and state–space equations are first determined by analyzing the motion features of the camera and the object. The system is then represented as an autoregressive moving average with extra input (ARMAX) and a valid estimation model. The adaptive predictor estimates online the relevant 3D parameters between the camera and the object, which are subsequently used to calculate the system sensitivity of the neural network. The ELM–PID controller is designed for adaptive adjustment of control parameters, and the scheme was validated on a physical robot platform. The experimental results showed that the proposed method’s vision-tracking control displayed superior performance to pure P and PID controllers.
ISSN:2075-1702
2075-1702
DOI:10.3390/machines10090782