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국태용 木浦大學校 工業技術硏究所 1993 工業技術硏究誌 Vol.3 No.-
An iterative learning scheme comprising a unique feedforward learning controller and a linear feedback controller is presented. In the feedback loop, the fixed-gain controller provides a stable open neighborhood along a desired trajectory. In the feedforward path, on th other hand, a learning control strategy is exploited to predict the desired actuator torques. It is shown that the predicted actuator torque converges to the desired one as the iteration number increases. The convergence is established based on the Lyapunov stability theory. The proposed learning scheme is structurely simple and computationaly efficient. Moreover, it posesses two major advantages : the ability to reject unknown deterministic disturbances and the ability to adapt itself to the unknown system parameters.
로봇 머니퓰레이터의 기구학 제어를 위한 연관사상 학습제어기
정재욱,국태용,이택종 成均館大學校 科學技術硏究所 1997 論文集 Vol.48 No.1
In this paper, two specially designed associative mapping memories, called Associative Mapping Elements(AME) and Multiple-Digit Overlapping AME(MDO-AME), are presented for learning of nonlinear functions including kinematics of robot manipulators. The proposed associative mapping memories consist of associative mapping rules(AMR) and weight update rules(WUR) which guarantee generalization and specialization of input-output relationship of learned nonlinear functions. Two simulation results, one for supervised learning and the other for unsupervised learning, are given to demonstrate the effectiveness of the proposed associative mapping memories.
박정동,이현배,국태용 成均館大學校 科學技術硏究所 1997 論文集 Vol.48 No.1
In this paper, we present a nonlinear adaptive controller for position tracking of induction motors. In constructing the adaptive controller, a backstepping approach is used under the condition of full state information, while a nonlinear observer is adopted for rotor flux estimation. The adaptive controller is shown to drive the state variables of system to the desired ones asymptotically and whose effectiveness is also shown via computer simulation.