A Study on Trajectory Control of PUMA Robot using Chaotic Neural Networks and PD Controller

카오틱 신경망과 PD제어기를 이용한 푸마 로봇의 궤적제어에 관한 연구

  • Jang, Chang-Hwa (Dept.of Electronics Engineering, Kumoh National Institute of Technology) ;
  • Kim, Sang-Hui (Dept.of Electronics Engineering, Kumoh National Institute of Technology) ;
  • An, Hui-Uk (Dept.of Electronics Engineering, Kumoh National Institute of Technology)
  • 장창화 (금오공과대학교 전자공학부) ;
  • 김상희 (금오공과대학교 전자공학부) ;
  • 안희욱 (금오공과대학교 전자공학부)
  • Published : 2000.09.01

Abstract

This paper presents a direct adaptive control of robot system using chaotic neural networks and PD controller. The chaotic neural networks have robust nonlinear dynamic characteristics because of the sufficient nonlinearity in neuron itself, and the additional self-feedback and inter-connecting weights between neurons in same layer. Since the structure and the learning method are not appropriate for applying in control system, this neural networks have not been applied. In this paper, a modified chaotic neural networks is presented for dynamic control system. To evaluate the performance of the proposed neural networks, these networks are applied to the trajectory control of the three-axis PUMA robot. The structure of controller consists of PD controller and chaotic neural networks in parallel for conforming the stability in initial learning phase. Therefore, the chaotic neural network controller acts as a compensating controller of PD controller.

본 논문은 카오틱 신경망과 PD 제어기를 이용한 로봇 시스템의 직접적응제어 방식에 관한 것이다. 카오틱 신경망은 상·하층 결합계수 외에 궤환 결합계수와 동일 층 내의 결합계수를 가지며, 뉴런자체의 충분한 비선형성 때문에 강한 동적특성을 가지고 있다. 그러나 신경망의 구조 및 학습의 문제점으로 인하여 동적 시스템의 제어에 적용되지 못하고 있다. 본 논문에서는 기존의 카오틱 신경망을 제어 분야에 적용하기 위하여 적합한 구조로 수정하고 수정된 신경망의 학습에 관하여 고찰하였다. 제안된 신경망은 모의 실험을 통하여 3 축 푸마 로봇의 경로 제어에 적용하였다. 카오틱 신경망 제어기는 PD 제어기와 병렬로 구성하여 학습 초기의 안정성을 확보하였고, 제어대상의 비선형성을 보상하는 보상 제어기의 역할을 수행하도록 하였다

Keywords

References

  1. J.J. Craig, P. Hsu, and S. Sastry, 'Adaptive Control of Mechanical Manipulators', IEEE Int. Conf on Robotics Automat., SF, CA, 1986
  2. J. J. E. Slotine and W. Li, 'Adaptive Manipulator Control: A Case Study', IEEE Trans. on Automatic Control, Vol. 33, No. 11, pp. 995-1003, November 1988 https://doi.org/10.1109/9.14411
  3. M. Kawato, Y. Uno, M. Isobe, and R. Suzuki, 'Hierarchical Neural Network Model for Voluntary Movement with Application to Robotics,' IEEE Control Systems Magazine, pp. 8-16, April 1988 https://doi.org/10.1109/37.1867
  4. H. Miyamoto, M. Kawato, T. Setyama, and R. Suzuki, 'Feedback Error Learning Neural Network for Trajectory Control of a Robotic Manipulator,' Neural Networks, Vol. 1, No.3, pp. 251-265, 1988 https://doi.org/10.1016/0893-6080(88)90030-5
  5. T. Ozaki, T. Suzuki, T. Furuhashi, S. Okuma, and Y. Uchikawa, 'Trajectory Control of Robotic Manipulators Using Neural Networks,' IEEE Trans. on Industrial Electronics, Vol. 38, No.3, pp. 195-202, June 1991 https://doi.org/10.1109/41.87587
  6. T. Yabuta and T. Yamada, 'Neural Network Controller Characteristics with Regard to Adaptive Control,' IEEE Trans. on Systems, Man, and Cybernetics, Vol. 22, No.1, pp. 170-177, 1992 https://doi.org/10.1109/21.141322
  7. L. Jin, P. N. Nikiforuk, and M. M. Gupta, 'Dynamics and Stability of Multilayered Recurrent Neural Networks,' Proc. of Int. Joint Conf. on Neural Networks, pp. 1135-1140, SF, CA, 1993 https://doi.org/10.1109/ICNN.1993.298717
  8. Y. Fang and T. J. Sejnowski, 'Faster Learning for Dynamic Recurrent Backpropagation', Neural Computation, Vol. 2, No 3, pp. 270-273, 1990
  9. E. R. Caianiello, 'Outline of a Theory of Thought-Processes and Thinking Machines,' J. Theor. Biol., Vol 2. pp 204-235, 1961 https://doi.org/10.1016/0022-5193(61)90046-7
  10. J. Nagumo and S. Sato, 'On a Response Characteristics of a Mathematical Neuron Model,' Kybernetik 10, pp. 155-164, 1972 https://doi.org/10.1007/BF00290514
  11. K. Aihara, T. Takabe and M. Toyoda, 'Chaotic Neural Networks,' Phys. Lett. A144, pp. 333-340, 1990 https://doi.org/10.1016/0375-9601(90)90136-C
  12. K. Shimiza, K. Aihara and M. Kotani, 'An Electronic Circuit Model of Chaotic Neural Networks,' Electronics and Communications in Japan Part 3, Vol. 73. pp. 51-64 1991
  13. M. Adachi, K. Aihara, M. Kotani, 'An Analysis of Associative Dynamics in A Chaotic Neural Network with External Stimulation,' Proc. of IJCNN, pp. 409-412, 1993 https://doi.org/10.1109/IJCNN.1993.713943
  14. L. Chen, K. Aihara, 'Chaotic Annealing by a Neural Network Model with Transient Chaos', Neural Networks, Vol. 8, No.6, pp. 915-930, 1995 https://doi.org/10.1016/0893-6080(95)00033-V
  15. I. Tokuda, T. Nagashima, K. Aihara, 'Global Bifurcation of Chaotic Neural Networks and its Application to Traveling Salesman Problems', Neural Networks, Vol. 10, No.9, pp. 1673-1690, 1997 https://doi.org/10.1016/S0893-6080(97)00023-3
  16. T.Kwok, K.A.Smith, 'A Unified Frame Work for Chaotic Neural-Network Approaches to Combinational Optimization', IEEE Trans. on Neural Networks, Vol. 10, No.4, pp. 978-981, 1999 https://doi.org/10.1109/72.774279