2족 보행로봇의 실시간 작업동작 생성을 위한 지능제어에 관한 연구

다국어 초록 (Multilingual Abstract)

In this paper, we propose a new learning control scheme for various walk motion control of biped robot with same learning-base by neural network. We show that learning control algorithm based on the neural network is significantly more attractive intelligent controller design than previous traditional forms of control systems. A multi layer back propagation neural network identification is simulated to obtain a dynamic model of biped robot. Once the neural network has learned, the other neural network control is designed for various trajectory tracking control with same learning-base.
The biped robots have been received increased attention due to several properties such as its human like mobility and the high-order dynamic equation. These properties enable the biped robots to perform the dangerous works instead of human beings. Thus, the stable walking control of the biped robots is a fundamentally hot issue and has been studied by many researchers. However, legged locomotion, it is difficult to control the biped robots. Besides, unlike the robot manipulator, the biped robot has an uncontrollable degree of freedom playing a dominant role for the stability of their locomotion in the biped robot dynamics. From the simulation and experiments the reliability of iterative learning control was illustrated.

목차 (Table of Contents)

• 〈Abstract〉
• 1. 서론
• 2. 제어알고리즘
• 3. 성능실험 및 결과
• 4. 결론
• References

참고문헌 (Reference)

1 Tang, Z., "Trajectory Planning for Smooth Transition of a Biped Robot" 2455-2460, 2003

2 Hirai, K., "The development of Honda humanoid robot" 2 : 1321-1326, 1998

3 Qiang Huang, "Planning Walking Patterns for a Biped Robot" 2001

4 M. Vukobratovic, "On the Stability of BipedLocomotion" BME-17 (BME-17): 25-36, 1970

5 R. S. Sutton, "Neuronlike Adaptive Elements That Can Solve Difficult Learning Control Problems" smc-13 (smc-13): 834-846, 1983

6 Chang Seok Oh, "Neuro Computer" Naeha 2000

7 D. Sbarbaro, "Neural networks for Nonlinear Internal Model Control" 138 (138): 431-438, 1991

8 이성수, "Implementation of Self-adaptive System using the Algorithm of Neural Network Learning Gain" 제어·로봇·시스템학회 6 (6): 453-459, 2008

9 I. Kato, "Hydraulically Powered Biped Walking Machine with a High Carrying Capacity" 1972

10 R. B. McGhee, "Finite State Control of Quadruped Locomotion" 1966

11 Y. F. Zheng, "Design and motion control of practical biped robots" 3 (3): 70-77, 1988

12 D. I. Jones, "Connectionist Approach to PID Autotuning" 139 (139): 279-285, 1992

13 Sylvain Miossec, "A Simplified Stability Study for a Biped Walk with Underactuated and Overactuated Phases" 24 (24): 537-551, 2005

14 L. Kraft, "A Comparision Between CMAC Neural Network Control and Two Traditional Adaptive Control Systems" 36-43, 1990