• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1573-1576
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• 1997

In this paper we present an associative menory network(AMN) controller for learning of robot trajectories. We use AMN controller in order to improve the performance of conventional learning control, e.g. RCL, which had studied by Sadegh et al. Computer simulations show the feasibility and effectiveness of the proposed AMN controller.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.326-331
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• 1988

In this paper, an iterative leaning control algorithm for the linear discrete system is proposed. Based upon the parameter estimation method, the learning for good tracking control is acqured through a sequence of repetitive operations. A series of simulation are performed to show the validity of this algorithm.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.297-298
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• 2007

Repetitive systems stand for a kind of systems that perform a simple task on a fixed pattern repetitively and are widely spread in industrial fields. Hence, those systems have been of much interests by many researchers, especially in the field of iterative learning control (ILC). In this paper, we propose a finite-horizon tracking control scheme for linear time-varying repetitive systems with uncertain initial conditions. The scheme is derived both analytically and numerically for state-feedback systems and only numerically for output-feedback systems. Then, it is extended to stable systems with input constraints. All numerical schemes are developed in the forms of linear matrix inequalities. A distinguished feature of the proposed scheme from the existing iterative learning control is that the scheme guarantees the tracking performance exactly even under uncertain initial conditions. The simulation results demonstrate the good performance of the proposed scheme.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.1
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• pp.29-34
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• 2010

This paper presents an intelligent learning controller for repetitive walking motion of biped walking robot. The proposed learning controller consists of an iterative learning controller and a direct learning controller. In the iterative learning controller, the PID feedback controller takes part in stabilizing the learning control system while the feedforward learning controller plays a role in compensating for the nonlinearity of uncertain biped walking robot. In the direct learning controller, the desired learning input for new joint trajectories with different time scales from the learned ones is generated directly based on the previous learned input profiles obtained from the iterative learning process. The effectiveness and tracking performance of the proposed learning controller to biped robotic motion is shown by mathematical analysis and computer simulation with 12 DOF biped walking robot.

• 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.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2001.05a
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• pp.145-151
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• 2001

An extension of interaction matrix formulation to the problem of system and disturbance identification for a plant that is corrupter by both process and output disturbances is presented. With only an assumed upper bound on the order of the system and an assumed upper bound on the number of disturbance frequencies, it is shown that both the disturbance-free model and disturbance effect can be recovered exactly from disturbance-corrupted input-output data without direct measurement of the periodic disturbances. The rich information returned by the identification can be used by a performance-oriented model-based loaming or repetitive control system to eliminate unwanted periodic disturbances.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.1113-1119
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• 1989

Neural network control has many innovative potentials for intelligent adaptive control. Among many, it promises real time adaption, robustness, fault tolerance, and self-learning which can be achieved with little or no system models. In this paper, a dynamic robot controller has been developed based on a backpropagation neural network. It gradually learns the robot's dynamic properties through repetitive movements being initially trained with a PD controller. Its control performance has been tested on a simulated PUMA 560 demonstrating fast learning and convergence.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.85.4-85
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• 2001

A learning controller is presented for repetitive walking motion of biped robot. The learning control scheme learns the approximate inverse dynamics input of biped walking robot and uses the learned input pattern to generate an input profile of different walking motion from that learnt. In the learning controller, the PID feedback controller takes part in stabilizing the transient response of robot dynamics while the feedforward learning controller plays a role in computing the desired actuator torques for feedforward nonlinear dynamics compensation in steady state. It is shown that all the error signals in the learning control system are bounded and the robot motion trajectory converges to the desired one asymptotically. The proposed learning control scheme is ...

• Journal of the Korean Society of Industry Convergence
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• v.18 no.4
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• pp.224-230
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• 2015

In this paper, it is presented a learning controller for repetitive walking control of biped walking robot. We propose the iterative learning control algorithm which can learn periodic nonlinear load change ocuured due to the walking period through the intelligent control, 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 intelligent control to biped robotic motion is shown via dynamic simulation with 25-DOF biped walking robot.

• Journal of the Korean Society of Physical Medicine
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• v.7 no.1
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• pp.11-20
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• 2012

Purpose : This study is intended to examine the repetitive transcranial magnetic stimulation on cognitive function in the focal ischemic stroke rat model. Methods : This study selected 30 Sprague-Dawley rats of 8 weeks. The groups were divided into two groups and assigned 15 rats to each group. Control group: Non-treatment after injured by focal ischemic stroke; Experimental group: application of repetitive transcranial magnetic stimulation(0.1 Tesla, 25 Hz, 20 min/time, 2 times/day, 5 days/2 week) after injured by focal ischemic stroke. To assess the effect of rTMS, the passive avoidance test, spatial learning and memory ability test were analyzed at the pre, 1 day, $7^{th}$ day, $14^{th}$ day and immunohistochemistric response of BDNF were analyzed in the hippocampal dentate gyrus at $7^{th}$ day, $14^{th}$ day. Results : In passive avoidance test, the outcome of experimental group was different significantly than the control group at the $7^{th}$ day, $14^{th}$ day. In spatial learning and memory ability test, the outcome of experimental group was different significantly than the control group at the $7^{th}$ day, $14^{th}$ day. In immunohistochemistric response of BDNF in the hippocampal dentate gyrus, experimental groups was more increased than control group. Conclusion : These result suggest that improved cognitive function by repetitive transcranial magnetic stimulation after focal ischemic stroke is associated with dynamically altered expression of BDNF in hippocampal dentate gyrus and that is related with synaptic plasticity.