• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.450-453
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• 1997

An active control for the swing of crane systems is very important for increasing the productivity. This article introduces the control for the position and the swing of a crane using the fuzzy learning method. Because the crane is a multi-variable system, learning is done to control both position and swing of the crane. Also the fuzzy control rules are separately acquired with the loading and unloading situation of the crane for more accurate control. The result of simulations shows that the crane is just controlled for a very large swing angle of 1 radian within nearly one cycle.

• 전기학회논문지P
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• 제59권1호
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 하계종합학술대회 논문집
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• pp.101-105
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• 1989

The iterative learning controller makes the system output follow the desired output over a finite time interval through iterating trials. In this paper, first we discuss that the design problem of learning controller is originally the design problem of the inverse model. Then we show that the tracking error which is the difference between the desired output and the system output is reduced monotonically by properly modeled inverse system if the magnitude of the learning operator being introduced is bounded within the unit circle in complex domain. Also it would be shown that the conventional learning control method is a kind of extremely simplified inverse model learning control method of the objective controlled system. Hence this control method can be considered as a generalization of the conventional learning control method. The more a designer model the objective controlled system precisely, the better the performance of the approximated inverse model learning controller would be. Finally we compare the performance of the conventional learning control method with that of the approximated inverse model learning control method by computer simulation.

• 전기학회논문지P
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• 제55권2호
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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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.81-86
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• 1991

The iterative learning operation has been utilized in the temperature Control of a batch reactor. A generic form of feedback-assisted first-order learning control scheme was constructed and then various design and operation modes were derived through convergence and robustness analysis in the frequency domain. The proposed learning control scheme was then implemented on a bench scale batch reactor with the heat of reaction simulated by an electric heater. The results show a great improvement in the performance of control as the number of batch operations progressed.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.1277-1283
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• 1990

The iterative learning control synthesized in the frequency domain has been utilized for temperature control of a batch reactor. For this purpose, a feedback-assisted generalized learning control scheme was constructed first, and the convergence and robustness analyses were conducted in the frequency domain. The feedback-assisted learning operation was then implemented in a bench scale batch reactor where reaction heat is simulated using an electric heater. As a result, progressive reduction of temperature control error could be obviously observed as batch operation is repeated.

• 전자공학회논문지S
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• 제36S권2호
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• pp.28-37
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• 1999

재 강화 학습 방법을 다수의 제어입력을 가진 시스템에 대한 자율적 제어 기법 습득에 활용하기 위해서 차등책임 적응비평 학습구조를 제안하였다. 재 강화 학습은 여러 단계의 제어동작 끝에 얻어지는 최종 비평값을 활용하여 그 전에 행해졌던 제어 동작을 강화 혹은 약화 학습하는 자율적 학습방법이다. 대표적인 재강화학습 방법은 적응비평학습 구조를 이용하는 방법인데 비평모듈과 동작모듈을 이용하여 외부 비평 값을 최대로 활용함으로써 학습효과를 극대화시키는 방법이다. 이 학습방법에서는 단일한 제어입력을 갖는 시스템으로만 적용이 제한된다는 단점이 있다. 제안한 차등책임 적응비평 학습 구조에서는 비평함수를 제어 입력 인자의 함수로 구축한 다음 제어인자에 대한 차별 화된 비평 값을 부분미분을 통하여 산출함으로써 다수의 제어입력을 가진 시스템의 제어기술 학습이 가능하게 하였다. 제안한 학습제어 구조는 학습속도가 빠른 CMAC 신경회로망을 이용하여 구축하였으며 2개의 제어입력을 갖는 2-D Cart-Pole 시스템과 3 개의 제어입력을 갖는 인간구조 로봇시스템의 앉는 동작의 학습제어 시뮬레이션을 통하여 효용성을 확인하였다.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1996년도 추계학술대회 논문
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• pp.6-10
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• 1996

This paper presents an overview of research results obtained by the authors in a series of publications. Methods are developed both for time-varying and time-invariant for linear and nonlinear. for time domain and frequency domain . and for discrete-time and continuous-time systems. Among the topics presented are: 1. Learning control based on integral control concepts applied in the repetition domain. 2. New algorithms that give improved transient response of the indirect adaptive control ideas. 4. Direct model reference learning control. 5 . Learning control based frequency domain. 6. Use of neural networks in learning control. 7. Decentralized learning controllers. These learning algorithms apply to robot control. The decentralized learning control laws are important in such applications becaused of the usual robot decentralized controller structured.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국내학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
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• pp.67-71
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• 1988

We present an algorithm that uses trajectory following errors to improve a feedforward command to a robot in the iterative manner. It has been shown that when the manipulator handles an unknown object, the P-type learning algorithm can make the trajectory converge to a desired path and also that the proposed learning control algorithm performs better than the other type learning control algorithm. A numerical simulation of a three degree of freedom manipulator such as PUMA-560 ROBOT has been performed to illustrate the effectiveness of the proposed learning algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.354-359
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• 2003

In this paper, we consider the problem of designing a simple learning variable structure system for repeatable tracking control of robot manipulators. We combine a variable structure control law as the robust part for stabilization and a feedforward learning law as the intelligent part for nonlinearity compensation. We show that the tracking error asymptotically converges to zero. Finally, we give computer simulation results in order to show the effectiveness of our method.