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BELBIC을 이용한 Rotary Inverted Pendulum 제어
Control of a Rotary Inverted Pendulum System Using Brain Emotional Learning Based Intelligent Controller 원문보기

한국생산제조시스템학회지 = Journal of the Korean Society of Manufacturing Technology Engineers, v.22 no.5, 2013년, pp.837 - 844  

김재원 (Department of Bio-Nano System Engineering, Chonbuk National University) ,  오재윤 (Division of Mechanical System Engineering, Chonbuk National University)

Abstract ▼ AI-Helper 아이콘AI-Helper

This study performs erection of a pendulum hanging at a free end of an arm by rotating the arm to the upright position. A mathematical model of a rotary inverted pendulum system (RIPS) is derived. A brain emotional learning based intelligent controller (BELBIC) is designed and used as a controller f...

주제어

#BELBIC (Brain Emotional Learning Based Intelligent Controller)   #RIPS (Rotary Inverted Pendulum System)   #SI (Sensory Input)   #ES (Emotional Signal)   #End mass   #Adaptiveness   #Robustness  

AI 본문요약
AI-Helper 아이콘 AI-Helper

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제안 방법

  • Then, we performed a simulation with a pendulum attached an end mass in order to evaluate adaptiveness against a system variation of defined BELBIC. Finally, we performed a simulation with a motor control input disturbed by a noise in order to evaluate robustness against external disturbance of defined BELBIC.
  • In order to show adaptiveness of BELBIC, a simulation was performed with a pendulum attached an end mass, and with BELBIC used in the above simulation. The end mass has a weight of half of the pendulum.
  • In order to show robustness of BELBIC, a simulation was performed with motor control input in which a noise was added as external disturbance, and with BELBIC used in the above simulations. A pendulum with an end mass was used in the simulation.
  • First, we performed a simulation with a pendulum at which an end mass was not attached at the end the pendulum. Then, we performed a simulation with a pendulum attached an end mass in order to evaluate adaptiveness against a system variation of defined BELBIC. Finally, we performed a simulation with a motor control input disturbed by a noise in order to evaluate robustness against external disturbance of defined BELBIC.
  • This paper derives a mathematical model of a RIPS, and designs BELBIC as a controller for control the arm and pendulum of a RIPS. A simulation for evaluating adaptiveness of developed BELBIC for a system variation is performed.
  • This paper performs a study on erecting a pendulum hanging at a free end of an arm by rotating an arm to upright position using BELBIC. Control inputs of BELBIC are sensory input (SI), and internal reward signal.

이론/모형

  • Simulations for erecting a pendulum of a RIPS to upright position with BELBIC were performed. Simulations were performed with MATLAB Simulink[17]. Fig.
  • This paper derived a mathematical model of a RIPS. BELBIC was designed, and used for swing up the pendulum and balancing the pendulum at upright position.
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참고문헌 (17)

  1. Iraj, H., Saleh, M., Abbas, H., 2008, Input-Output Feedback Linearization Cascade Controller Using Genetic Algorithm for Rotary Inverted Pendulum System, American Journal of Applied Sciences 5:10 1322-1328. 

    상세보기 crossref

  2. Lachhab, N., Abbas, H., Werner, H., 2008, A neuralnetwork based technique for modelling and LPV control of an arm-driven inverted pendulum, Decision and Control 47th IEEE Conference 3860-3865. 

  3. Jung, S., Wen, J. T., 2004, Nonlinear Model Predictive Control for the Swing-Up of a Rotary Inverted Pendulum, Journal of Dynamic Systems Measurement and Control 126 666-673. 

    상세보기 crossref

  4. Kaise, N., Fujimoto, Y., 1999, Applying the evolutionary neural networks with genetic algorithms to control a rolling inverted pendulum, Springer Berlin Heidelberg 223-230. 

  5. Jung, D. Y., Kim, H. R., Han, S. H., 2004, Intelligent Controller of Mobile Robot Using Genetic Algorithm, KSMTE Spring Conference 2004 181-186. 

  6. Tack, H. H., Kim, M. G., 2001, The Stabilization control of inverted pendulum system using neuro fuzzy control algorithm, Agricultural Technology Institute of Jinju Industrial College 14 207-214. 

  7. Melba, M. P., Marimuthu, N. S., 2008, Design of intelligent hybrid controller for swing-up and stabilization of rotary inverted pendulum, ARPN Journal of Engineering and Applied Sciences 3:4 60-70. 

  8. Roh, S. B., Oh, S. K., 2001, The Design of hybrid fuzzy controller for inverted pendulum, KIEE Summer Conference 2702-2704. 

  9. Krishen, J., Becerra, V. M., 2006, Efficient fuzzy control of a rotary inverted pendulum based on LQR mapping, IEEE International Symposium on Intelligent Control 2701-2706. 

  10. Fallahi, M., Azadi, S., 2009, Adaptive Control of an Inverted Pendulum Using Adaptive PID Neural Network, IEEE International Conference on Signal Processing System 589-593. 

  11. Lucas, C., Shahmirzadi, D., Sheikholeslami, N., 2004, Introducing BELBIC : Brain Emotional Learning Based Intelligent Control, Intelligent Automation and Soft Computing 10:1 11-22. 

    상세보기 crossref

  12. Moren, J., 2002, Emotion and Learning, Doctorate Thesis, Department of Cognitive Science Lund University, Sweden. 

  13. Rashidi, F., Rashidi, M., Hashemi, A., 2003, Appling intelligent controllers for speed regulation of DC motor, The 11th Mediterranean Conference on Control and Automation 1-6. 

  14. Jafarzadeh, S., Mirheidari, R., Jahed-Motlagh, M. R., Barkhordari, M., 2008, Designing PID and BELBIC controllers in path tracking problem, International Journal of Computers Communications & Control 3 343-348. 

    crossref

  15. Valizadeh, S., Jamali, M. R., Lucas, C., 2008, A particleswarm- based approach for optimum design of BELBIC controller in AVR system, ICCAS 2008 International Conference on. IEEE 2679-2684. 

  16. Arpit, J., 2009, Computational modeling of the brain limbic system and its application in control engineering, Master Thesis, Engineering in Electronics Instrumentation & Control Engineering to Thapar University, India. 

  17. Matlab simulink, 2009, http://www.mathworks.co.kr/products /simulink/index.html. 

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