Improving the Accuracy of a Robot by Using Neural Networks (Neural Compensators and Nonlinear Dynamics)

The subject of this paper is a programmable con trol system for a robotic manipulator. Considering the complex nonlinear dynamics involved in practical applications of systems and robotic arms, the traditional control method is here replaced by the designed Elma and adaptive radial basis function ne...

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Bibliographic Details
Published inRobotics (Basel) Vol. 11; no. 4; p. 83
Main Authors Yan, Zhengjie, Klochkov, Yury, Xi, Lin
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.08.2022
Subjects
Adaptive control
Algorithms
Approximation
Compensators
Control algorithms
Control methods
Control systems
Controllers
Design
dynamic analysis
Dynamical systems
modeling
neural network
Neural networks
Nonlinear dynamics
nonlinear multivariate compensators
programmable control system
Radial basis function
Robot arms
robot manipulator
Robotics
Robots
Systems stability
Russia
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ISSN2218-6581
2218-6581
DOI10.3390/robotics11040083

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Summary:The subject of this paper is a programmable con trol system for a robotic manipulator. Considering the complex nonlinear dynamics involved in practical applications of systems and robotic arms, the traditional control method is here replaced by the designed Elma and adaptive radial basis function neural network—thereby improving the system stability and response rate. Related controllers and compensators were developed and trained using MATLAB-related software. The training results of the two neural network controllers for the robot programming trajectories are presented and the dynamic errors of the different types of neural network controllers and two control methods are analyzed.
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ISSN:2218-6581
2218-6581
DOI:10.3390/robotics11040083