• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.538-538
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• 2000

This paper presents a learning controller for repetitive gate control of biped robot. The learning control scheme consists of a feedforward learning rule and linear feedback control input for stabilization of learning system. The feasibility of teaming control to biped robotic motion is shown via dynamic simulation with 12 dof biped robot.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.56 no.4
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• pp.161-167
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• 2007

This paper introduces a dynamic walking control of biped walking robot using intelligent sensor interface and shows an intelligent control method for biped walking robot. For the dynamic walking control of biped walking robot, serious motion controllers are used. They are main controller(using INTEL80C296SA MPU), sub controller(using TMS320LF2406 DSP), sensor controller(using Atmega128 MPU) etc. The used sensors are gyro sensor, tilt sensor, infrared sensor, FSR sensor etc. For the feasibility of a dynamic walking control of biped walking robot, we use the biped walking robot which has twenty-five degrees of freedom(D.O.F.) in total. Our biped robot is composed of two legs of six D.O.F. each, two arms of five D.O.F. each, a waist of two D.O.F., a head of one D.O.F.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1464-1468
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• 2004

This paper presents a learning controller for repetitive gait control of biped walking robot. We propose the iterative learning control algorithm which can learn periodic nonlinear load change ocuured according to the walking period through the iterative learning, not calculating the complex dynamics of walking robot. The learning control scheme consists of a feedforward learning rule and linear feedback control input for stabilization of learning system. The feasibility of learning control to biped robotic motion is shown via dynamic simulation with 12-DOF biped walking robot.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.594-596
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• 2005

This paper present a learning controller for repetitive gate control of biped robot. The learning control scheme consists of a feedforward learning rule and linear feedback control input for stabilization of learning system. The feasibility of learning control to biped robotic motion is shown via dynamic simulation and experimental results with 24 DOF biped robot.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.311-314
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• 1995

Biped locomotion can be simply modeled as a linear inverted pendulum mode. This model considers only the CG (center of gravity) of the entire system. But in real biped robot systems, the free-leg motion dynamics is not negligible. So if its dynamics is not considered in designing the reference CG motion, it is badly influence to the ZMP(zero moment point) position of the biped robot walking in the sagittal plane. Therefore, we modeled the biped locomotion similar to the linear inverted pendulum mode but considered the predetermined free-leg dynamics. To verify that the proposed biped locomotion is more stable than the linear inverted pendulum mode, we constructed a biped robot simulator and designed a serco controller to track both the reference motion of the free leg and the reference motion of CG of the biped robot using the computed torque control low. And through simulations, we verified that the proposed walking is better in stability than the one based on the linear inverted pendulum mode.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.11
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• pp.1081-1087
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• 2006

Generally, biped walking is difficult to control because a biped robot is a nonlinear system with various uncertainties. In this paper, we propose a hybrid sliding-mode control method using a WNN uncertainty observer for stable walking of the 5-link biped robot with model uncertainties and the external disturbance. In our control system, the sliding mode control is used as main controller for the stable walking and a wavelet neural network(WNN) is used as an uncertainty observe. to estimate uncertainties of a biped robot model, and the error compensator is designed to compensate the reconstruction error of the WNN. The weights of WNN are trained by adaptation laws that are induced from the Lyapunov stability theorem. Finally, the effectiveness of the proposed control system is verified through computer simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.3
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• pp.269-276
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• 2011

This paper proposes a novel control method using phase plane for a hydraulic biped humanoid robot. In biped walking control, it is much more difficult to control the posture of a biped robot in the coronal plane because the supporting area formed by the both feet in the coronal plane is much narrower than that of the sagittal plane. When the biped robot walks stably, the phase portrait of the pelvis in the coronal plane makes an elliptical shape. From this point of view, we develop an ankle torque controller and a foot placement controller for tracking the desired phase portrait during walking. We design these controllers by using simulations of a simplified compass gait biped model to regulate the desired phase portrait of pelvis. The effectiveness of the proposed control method is proved through full-body dynamic walking simulations and real experiments of the SARCOS hydraulic biped humanoid.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2573-2576
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• 2003

This paper proposes the controller for biped robot using intelligent control algorithm. The main purpose of this paper is to design the robot controller using Hierarchical Mixture of Experts(HME). The neural network direct control method will be applied to the control scheme for the biped robot and neural network will learn the dynamics of biped robot. The teaming scheme using a intelligent controller to biped robot is developed. The teaming scheme uses a HME controller combined with a inverse biped robot model. The controller provides the control signals at each control time instant. Simulation results are reported for a seven-link biped robot.

• International Journal of Control, Automation, and Systems
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• v.5 no.3
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• pp.269-282
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• 2007

This paper presents a robust control method via the intelligent adaptive backstepping design technique for stable walking of nine-link biped robots with unknown model uncertainties and external disturbances. In our control structure, the self recurrent wavelet neural network(SRWNN) which has the information storage ability is used to observe the uncertainties of the biped robots. The adaptation laws for all weights of the SRWNN are induced from the Lyapunov stability theorem, which are used for on-line controlling biped robots. Also, we prove that all signals in the closed-loop adaptive system are uniformly ultimately bounded. Through computer simulations of a nine-link biped robot with model uncertainties and external disturbances, we illustrate the effectiveness of the proposed control system.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.55 no.2
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• pp.83-88
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• 2006

This paper proposes a method that minimizes the consumed energy by searching the optimal locations of the mass centers of the biped robot's links using Genetic Algorithm. This paper presents a learning controller for repetitive gait control of the biped robot. The learning control scheme consists of a feedforward learning nile and linear feedback control input for stabilization of learning system. The feasibility of learning control to the biped robotic motion is shown via computer simulation and experimental results with 24 DOF biped walking robot.