• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.111-114
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• 1996

This paper proposes a robust control method using Universal Learning Network(U.L.N.) and second order derivatives of U.L.N.. Robust control considered here is defined as follows. Even if external input (equal to reference input in this paper) to the system at control stage changes awfully from that at learning stage, the system can be controlled so as to maintain a good performance. In order to realize such a robust control, a new term concerning the perturbation is added to a usual criterion function. And parameter variables are adjusted so as to minimize the above mentioned criterion function using the second order derivative of the criterion function with respect to the parameters.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.14-19
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• 1993

In this paper, we proposed an indirect learning and direct adaptive control schemes using neural networks, i.e., composite adaptive neural control, for a class of continuous nonlinear systems. With the indirect learning method, the neural network learns the nonlinear basis of the system inverse dynamics by a modified backpropagation learning rule. The basis spans the local vector space of inverse dynamics with the direct adaptation method when the indirect learning result is within a prescribed error tolerance, as such this method is closely related to the adaptive control methods. Also hash addressing technique, similar to the CMAC functional architecture, is introduced for partitioning network hidden nodes according to the system states, so global neuro control properties can be organized by the local ones. For uniform stability, the sliding mode control is introduced when the neural network has not sufficiently learned the system dynamics. With proper assumptions on the controlled system, global stability and tracking error convergence proof can be given. The performance of the proposed control scheme is demonstrated with the simulation results of a nonlinear system.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.112-115
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• 1989

This paper proposes a learning control algorithm for trajectory tracking of large scale system. The controller using only localized informations is composed of stabilizing controller and iterative learning controller. Stabilization and convergence of each subsystem is assured under some conditions which are inequalities of inter-connection terms and learning controller gain.

• Journal of Drive and Control
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• v.19 no.4
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• pp.110-116
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• 2022

Recently, various studies have been conducted to apply deep learning and AI to various fields of autonomous driving, such as recognition, sensor processing, decision-making, and control. This paper proposes a controller applicable to path following, static obstacle avoidance, and pedestrian avoidance situations by utilizing reinforcement learning in autonomous vehicles. For repetitive driving simulation, a reinforcement learning environment was constructed using virtual environments. After learning path following scenarios, we compared control performance with Pure-Pursuit controllers and Stanley controllers, which are widely used due to their good performance and simplicity. Based on the test case of the KNCAP test and assessment protocol, autonomous emergency steering scenarios and autonomous emergency braking scenarios were created and used for learning. Experimental results from zero collisions demonstrated that the reinforcement learning controller was successful in the stationary obstacle avoidance scenario and pedestrian collision scenario under a given condition.

• Journal of Engineering Education Research
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• v.21 no.1
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• pp.37-43
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• 2018

The purpose of this study is to investigate the effects of peer tutoring and feedback on academic learning in university mathematics. We compared subject satisfaction and academic achievement between the test group and the control group. We classified the test group(82 participants) and the control group(134 non-participants) and then applied peer tutoring and feedback to the test group. The rest of the environment was the same except for participation in the program. According to results, it was confirmed that the subject satisfaction were significantly higher(significance level .05) in the test group, where the subject satisfaction were learning objectives and expectation, learning satisfaction, and learning effect. Furthermore, in the change of academic achievement, the rate of decrease was lower and the rate of increase was higher in the test group than the control group. The satisfaction of participants was 4.33(Likert scale 5), and this trend tended to be same regardless of gender, high school course, or admission process.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.358-358
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• 2000

An iterative learning control technique based on a linear quadratic optimal criterion is proposed for temperature uniformity control of a silicon wafer in rapid thermal processing.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1520-1526
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• 1999

This paper presents a current feedback learning controller for dynamic control of DC motors. The proposed controller uses the full third-order dynamics model of DC motor system to drive stable learning rules for virtual current learning input, voltage learning input, and the coefficient of electromotive force. It is shown that the proposed learning controller drives the state of uncertain DC motor system with unknown system parameters and external load torque to the desired one globally asymptotically. Computer simulation and experimental results are given to demonstrate the effectiveness of the proposed adaptive learning controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1082-1085
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• 1993

In this paper, we notice the fact that a human learning process is characterized by a process under a natural language environment, and discuss an approach of learning based on indirect linguistic instructions. An instruction is interpreted through some meaning elements and each trend. Fuzzy evaluation rule are constructed for the searched meaning elements of the given instruction, and the performance of a system to be learned is improved by the evaluation rules. In this paper, we propose a framework of learning based on indirect linguistic instruction based learning using fuzzy theory: FULLINS(FUzzy-Learning based on Linguistic IN-Struction). The validity of FULLINS is shown by applying it to helicopter flight control.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.168-172
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• 1996

In master-slave teleoperating system, it is important that the system has good maneuverability. In this paper, it is addressed an adaptive learning control method applicable to the master-slave system. This control scheme has the ability to estimate uncertain dynamic parameters included intrinsically in the system and to achieve the desired performance without the nasty matrix operation. The proposed method is applied to a master-slave teleoperating system composed of two SCARA robots and verified experimentally.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.234-239
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• 1990

A neural controller for process control was proposed that combines a simple feedback controller with a neural network. This control was applied to distillation control. The feedback error learning technique was used for on-line learning. Important characteristics on neural controller were analyzed. The proposed neural controller can cope well with strong interactions, significant time delays, sudden changes in process dynamics without any prior knowledge of the process. It was shown that the neural controller has good features such as fault tolerance, interpolation effect and random learning capability