Neural Network Learning Algorithms for High-Precision Position Control and Drift Attenuation in Robotic Manipulators

In this paper, different learning methods based on Artificial Neural Networks (ANNs) are examined to replace the default speed controller for high-precision position control and drift attenuation in robotic manipulators. ANN learning methods including Levenberg–Marquardt and Bayesian Regression are...

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Bibliographic Details
Published in:Applied sciences 2023-10, Vol.13 (19), p.10854
Main Authors: Mystkowski, Arkadiusz, Wolniakowski, Adam, Kadri, Nesrine, Sewiolo, Mateusz, Scalera, Lorenzo
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Controllers
Data mining
Datasets
Deep learning
Genetic algorithms
Kinematics
learning algorithm
Machine learning
Measuring instruments
Methods
neural network
Neural networks
Robotics
Robotics industry
Robots
Simulation
Simulation methods
trajectory tracking
UR5 robot
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Summary:In this paper, different learning methods based on Artificial Neural Networks (ANNs) are examined to replace the default speed controller for high-precision position control and drift attenuation in robotic manipulators. ANN learning methods including Levenberg–Marquardt and Bayesian Regression are implemented and compared using a UR5 robot with six degrees of freedom to improve trajectory tracking and minimize position error. Extensive simulation and experimental tests on the identification and control of the robot by means of the neural network controllers yield comparable results with respect to the classical controller, showing the feasibility of the proposed approach.
ISSN:2076-3417
2076-3417
DOI:10.3390/app131910854