The Transactions of the Korean Institute of Power Electronics (전력전자학회논문지)

The Korean Institute of Power Electronics (전력전자학회)
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Simple Robust Digital Position Control Algorithm of BLDD Motor using Neural Network with State Feedback

상태궤환과 신경망을 이용한 BLDD Motor의 간단한 강인 위치 제어 알고리즘

  • 고종선 (원광대학교 공과대학 전기전자공학부) ;
  • 안태천 (원광대학교 공과대학 전기전자공학부)
  • Published : 1998.09.01
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Abstract

A new control approach using neural network for the robust position control of a BRUSHLESS direct drive(BLDD) motor is presented. The linear quadratic controller plus feedforward neural network is employed to obtain the robust BLDD motor system approximately linearized using field-orientation method for an AC servo. The neural network is trained in on-line phases and this neural network is composed by a feedforward recall and error back-propagation training. Since the total number of nodes are only eight, this system will be easily realized by the general microprocessor. During the normal operation, the input-output response is sampled and the weighting value is trained by error back-propagation at each sample period to accommodate the possible variations in the parameters or load torque. And the state space analysis is performed to obtain the state feedback gains systematically. In addition, the robustness is also obtained without affecting overall system response.

직접 구동용 브러시 없는 직류전동기(BRUSHLESS direct drive motor : BLDD motor)의 강인한 위치제어를 위해 신경망을 사용하여 접근하는 새로운 제어방식이 소개된다. 전향 신경망이 추가된 선형 2차 제어기는 AC서보의 객체지향 방법을 사용함으로서 대략적으로 선형화 되어지는 강인한 BLDD 모터 시스템을 얻기 위해 사용된다. 구동 상태의 온-라인 위상에서 학습되는 이 신경망은 전향신호와 오차 역 전파법(Back-Propagation Method)에 의해 구성된다. 총 노드의 수가 8개이기 때문에 이 시스템은 일반적인 마이크로 프로세서에 의해 쉽게 실현될 수 있다. 일반적인 작동중, 입출력 응답은 표본화되어지고 가중치는 매개변수 또는 부하 토크의 능한 변이를 적용하기 위해 각 표본주기에서 오차 역 전파법에 의해 학습된다. 그리고, 상태공간에서 시스템 분석은 상태 궤환 이득을 얻기 위해 체계적으로 실행했다. 또한, 강인성은 전반적인 시스템응답에 영향력을 주지 않고 얻어진다.

Keywords

References

  1. Analysis of Electric Machinery Paul C. Krause
  2. Proc. of IECON 89 VSSTYPE SELF-TUNING CONTROL OF DIRECTDRIVE MOTOR K. Furuta;K. Kosuge;H. Kobayyashi
  3. Parallel Distributed Processing J. McClell;D. Rumelhart
  4. RhD Thesis Harvard New Tools for Prediction and Analysis in the Behavioral Sciences P. Werbos
  5. IEEE Control Systme Magazine Back-Propagation Neural Networks for Nonlinear Self-Tuning Adaptive Control Chen, F.
  6. IAS-Tutorial Course Introduction to Field orientation and High Performance AC Drives D.W. Novotny;R.D. Lorentz
  7. INT. J. System Scince v.5 no.12 Design of Linear Mulivairiable Continuous-Time Tracking System B. Poter;A. Bradshaw
  8. IEEE Proceedings v.58 The Synthesis of Regulator Logic using State-Variable Concept Arthur E.Bryson JR;David G.Luenberger
  9. Neural Networks v.2 Multi-layer Feedfirward Networks are Universal Appoximators Hornick K.;Stinchcombe M.;White H.
  10. Introduction to Artficial Neural System Jacek M. Zurada
  11. IEEE Trans. Ind. Electron. v.IE-40 A Robust Digital Position Control of Bruchless DC Motor with Dead Beat Load Torque Observer J. S. Ko;J. H. Lee;S. K. Chung;M. J. Youn
  12. IEEE Transaction on Industrial Electronics v.41 no.5 Continuous Variable Controller for BLDDSM Position Control with Prescribed Tracking Performance Jung Hoon Lee;Jong Sun Ko;Se Kyo Chung;Dae Sik Lee;Ju Jang Lee;Myung Joong Youn
  13. IEE Proc. Electric Power Applications v.141 no.2 A Pobust Position Control of Brushless DC motor with Adaptive Load Torque Observer Jong Sun Ko;Jung Hoon Lee;Myung Joong Youn
  14. IEE Proc. Electric Power Applications v.142 no.6 Robust speed Control of Brushless Direct-Drive Motor Using Integral Variable Structure control K. Chung;J. H. Lee;J. S. Ko;Prof. M. J. Youn
  15. International Journal of Electronics v.80 no.2 MRAC Load Torque Observer for Position Control of Blushless DC Motor Jong Sun Ko;Young Seok Jung;Myung Joong Youn
  16. International Journal of MECHATRONICS v.8 no.1 Simple Robust Position Control of BLDD Motor using Integral-Proportional Plus Fuzzy Speed Controller Jong Sun Ko;Myung Joong Youn