Earticle

초록

영어

Nowadays multiple control methods are used in robot control systems. A model, predictor or error estimator is often used as feedback controller to control a robot. While robots have become more and more intensive with algorithms capable to acquiring independent knowledge from raw data. This paper represents experimental results of real time machine learning control that does not require explicit knowledge about the plant. The controller can be applied on a broad range of tasks with different dynamic characteristics. We tested our controller on the balancing problem of a single propeller pendulum. Experimental results show that the use of a supervised machine learning algorithm in a single propeller pendulum allows the stable swing of a given angle.

목차

Abstract
 1. Introduction
 2. Supervised machine learning method
 3. Defining the Cost function
 4. Parameter learning with Gradient descent
 5. Experimental Result
 6. Conclusion
 References

키워드

저자

• TserendondogTengis [ Mongolian University of Science and Technology, Ulaanbaatar, Mongolia ] Corresponding author
• Amar Batmunkh [ Mongolian University of Science and Technology, Ulaanbaatar, Mongolia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380