• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.4
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• pp.49-60
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• 1996

The objective of this paper is to develop an adaptive learning method for fuzzy hypercubes using a neural network. An intelligent control system is proposed by exploiting only the merits of a fuzzy logic controller and a neural network, assuming that we can modify in real time the consequential parts of the rulebase with adaptive learning, and that initial fuzzy control rules are established in a temporarily stable region. We choose the structure of fuzzy hypercubes for the fuzzy controller, and utilize the Perceptron learning rule in order to upda1.e the fuzzy control ru1c:s on-line with the output errors. As a result, the effectiveness and the robustness of this intelligent controller are shown with application of the proposed adaptive fuzzy-neuro controller to control of the cart-pole system.

• The Transactions of the Korean Institute of Power Electronics
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• v.6 no.3
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• pp.273-281
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• 2001

In this paper, we propose a CMAC(Cerebellar Model Articulation Controller) neural network for controlling a non-linear system. CMAC is a neural network that models the human cerebellum. CMAC uses a table look-up method to resolve the complex non-linear system instead of numerical calculation method. It is very fast learn compared with other neural networks. It does not need a calculation time to generate control signals. The simulation results show that the proposed CMAC controllers for a simple non-linear function and a DC Motor speed control reduce tracking errors and improve the stability of its learning controllers. The validity of the proposed CMAC controller is also proved by the real-time tension control.

• Journal of IKEEE
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• v.17 no.2
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• pp.168-175
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• 2013

The delay times due to the propagating of data on PCB depend on the shape and length of interconnection lines when memory controllers and high speed memories are soldered on the PCB. The dependency on the placement and routing on the PCB requires redesign of I/O logic or reconfiguration of the memory controller after the delay time is measured if the controller is programmable. In this paper, we propose architecture of configuring logic for the delay time estimation by writing and reading test patterns while initializing the memories. The configuration logic writes test patterns to the memory and reads them by changing timing until the correct patterns are read. The timing information is stored and the configuration logic configures the memory controller at the end of initialization. The proposed method enables easy design of systems using PCB by solving the problem of the mismatching caused by the variation of placement and routing of components including memories and memory controllers. The proposed method can be applied to high speed SRAM, DRAM, and flash memory.

• Transactions of the Korean Society of Mechanical Engineers
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• v.13 no.6
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• pp.1156-1169
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• 1989

본 연구에서는 용접도중 발생할 수 있는 용접질 저항요인을 전극분리현상을 측정하여 파악하고 용접 열입력에 해당하는 용접전류를 학습제어방식(self-learning control)에 의하여 컴퓨터와 주변기기(interface)를 통해 조절함으로서 요구되는 균일한 용접질이 항상 보장되도록 하였다. 여기서 학습제어방식을 태택한 이유는 제어하고자 하는 대상의 동적 모델(dynamic model)이 없어도 제어기 이득의 선정이 비교적 자유롭고 용접 제어장치가 자체적으로 감지(monitoring)한 신호로 판단하여 제어동작을 취함으로서 용접시 축적되는 정보(data)가 용접기에 일종의 지능을 부여할 수 있어서 진보된 개념의 용접제어장치 개발의 가능성을 검토해 보기 위함이다.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.106-116
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• 1998

A position control algorithm of a flexible manipulator is studied. The proposed algorithm is based on an ACFAC(Automatic Constructed Fuzzy Adaptive Controller) system based on the neural network learning algorithms. The proposed system learns membership functions for input variables using unsupervised competitive learning algorithm and output information using supervised outstar learning algorithm. ACFAC does not need a dynamic modeling of the flexible manipulator. An ACFAC is designed that the end point of the flexible manipulator tracks the desired trajectory. The control input to the process is determined by error, velocity and variation of error. Simulation and experiment results show a robustness of ACFAC compared with the PID control and neural network algorithms.

• Journal of the Korean Institute of Intelligent Systems
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• v.2 no.1
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• pp.17-32
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• 1992

The multi-layer neural network that has broadly been utilized in designing the controller of robot manipulator possesses the desirable characteristics of learning capacity, by which the uncertain variation of the dynamic parameters of robot can be handled adaptively, and parallel distributed processing that makes it possible to control on real-time. However the error back propagation algorithm that has been utilized popularly in the learning of the multi-layer neural network has the problem of its slow convergence speed. In this paper, an approach to improve the convergence speed is proposed using the fuzzy logic that can effectively handle the uncertain and fuzzy informations by linguistic level. The effectiveness of the proposed algorithm is demonstrated by computer simulation of PUMA 560 robot manupulator.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.3 s.174
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• pp.653-660
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• 2000

The automatization by industrial robot of today is merely rely on to the simple position repeating works, but requirements of research and development to the force control which would adapt positively to various restriction or contacting works to environment. In this paper, a learning control algorithm using, neural networks is proposed for the position and force control by a direct-drive robot. The proposed controller is the feedback controller to which the learning function of neural network is added on to and has a character of improving controller's efficiency by learning. The effectiveness of the proposed algorithm is demonstrated by the experiment on the hybrid position and force control of a parallelogram link robot with a force sensor.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.11
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• pp.1423-1428
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• 2011

This paper proposes a learning input shaper with nonlinear actuator dynamics to reduce the residual vibration of flexible systems. The controller is composed of an estimator of the time constant of the nonlinear actuator dynamics, a recursive least squares method, and an iterative updating algorithm. The updating mechanism is modified by introducing a vibration measurement function to cope with the dynamics of nonlinear actuators. The controller is numerically evaluated with respect to parameter convergence and control performance by using a benchmark pendulum system. The feasibility and applicability of the controller are demonstrated by comparing its control performance to that of an existing controller algorithm.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.14-15
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• 2021

물리 시뮬레이션 기반의 캐릭터 동작 제어 문제를 강화학습을 이용하여 해결해 나가는 연구들이 계속해서 진행되고 있다. 이에 따라 이 문제를 강화학습을 이용하여 풀 때, 영향을 미치는 요소에 대한 연구도 계속해서 진행되고 있다. 우리는 지금까지 이뤄지지 않았던 상태 표현 방식에 따른 강화학습에 미치는 영향을 분석하였다. 첫째로, root attached frame, root aligned frame, projected aligned frame 3 가지 좌표계를 정의하였고, 이에 대해 표현된 상태를 이용하여 강화학습에 미치는 영향을 분석하였다. 둘째로, 동역학적 상태를 나타내는 캐릭터 관절의 위치, 각도에 따라 학습에 어떠한 영향을 미치는지 분석하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.2
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• pp.276-282
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• 2006

PID controller has been widely used in industry. Because it has a simple structure and robustness to modeling error. But it is difficult to have uniformly good control performance in system parameters variation or different velocity command. In this paper, we propose an adaptive PID controller based on a gradient descent learning. This algorithm has a simple structure like conventional PID controller and a robustness to system parameters variation and different velocity command. To verify performances of the proposed adaptive PID controller, the speed control of nonlinear DC motor is performed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system parameters variation.