Journal of Electrical Engineering and Technology

  • 제13권5호
  • /
  • Pages.2074-2079
  • /
  • 2018
  • /
  • 1975-0102(pISSN)
  • /
  • 2093-7423(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
권호 표지
DOI QR코드

DOI QR Code

Optimal Controller Design of One Link Inverted Pendulum Using Dynamic Programming and Discrete Cosine Transform

  • Kim, Namryul (Dept. of Electrical Engineering, Myongji University) ;
  • Lee, Bumjoo (Dept. of Electrical Engineering, Myongji University)
  • 투고 : 2017.10.23
  • 심사 : 2018.04.24
  • 발행 : 2018.09.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Global state space's optimal policy is used for offline controller in the form of table by using Dynamic Programming. If an optimal policy table has a large amount of control data, it is difficult to use the system in a low capacity system. To resolve these problem, controller using the compressed optimal policy table is proposed in this paper. A DCT is used for compression method and the cosine function is used as a basis. The size of cosine function decreased as the frequency increased. In other words, an essential information which is used for restoration is concentrated in the low frequency band and a value of small size that belong to a high frequency band could be discarded by quantization because high frequency's information doesn't have a big effect on restoration. Therefore, memory could be largely reduced by removing the information. The compressed output is stored in memory of embedded system in offline and optimal control input which correspond to state of plant is computed by interpolation with Inverse DCT in online. To verify the performance of the proposed controller, computer simulation was accomplished with a one link inverted pendulum.

키워드

참고문헌

  1. Busoniu, Lucian, et al., "Reinforcement learning and dynamic programming using function approximators," CRC Press, vol. 39, 2010, pp. 14-30.
  2. Jonathan P. How, "3. Dynamic programming : principle of optimality, dynamic programming, discrete LQR," Principle of Optimal Control, MIT OCW, pp. 1-27, 2008.
  3. Andrew Ng, "13. Reinforcement Learning and Control," CS 229 : Machine Learning, Stanford Univ., pp. 1-15, 2017.
  4. Michael Triantafyllou, "19. Linear Quadratic Regulator," Maneuvering and Control of Surface and Underwater Vehicles, MIT OCW, pp. 92-98, 2004.
  5. Atkeson, Christopher G, "Randomly sampling actions in dynamic programming, In Approximate Dynamic Programming and Reinforcement Learning, 2007. ADPRL 2007. IEEE International Symposium on, pp. 185-192, 2007.
  6. Atkeson, Chris and Benjamin Stephens, "Randomly sampling of states in dynamic programming," Advances in neural information processing systems, pp. 33-40, 2008.
  7. Atkeson, Christopher G. and Chenggang Liu, "Trajectory-based dynamic programming," Modeling, Simulation and Optimization of Bipedal Walking Cognitive Systems Monographs, pp. 1-15, 2013.
  8. Cabeen, K. and Gent, P., "Image Compression and the Discrete Cosine Transform," College of the Redwoods, Math 45, pp. 1-11, 1998.
  9. Andrew B. Watson, "Image Compression Using the Discrete Cosine Transform," Mathmatica Journal, vol. 4, pp. 81-88, 1994.
  10. Strang, G., "The discrete cosine transform," SIAM review, pp. 135-147, 1999.
  11. Bellman, Richard, "The theory of dynamic programming," RAND CORP SANTA MONICA CA, 1954, p. 1-8.