• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.103-106
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• 1996

This paper presents an iterative fuzzy learning control scheme which is applicable to a broad class of nonlinear systems. The control scheme achieves system stability and boundedness by using the linear feedback plus adaptive fuzzy controller and achieves precise tracking by using the iterative learning rules. The switching mode control unit is added to the adaptive fuzzy controller in order to compensate for the error that has been inevitably introduced from the fuzzy approximation of the nonlinear part. It also obviates any supervisory control action in the adaptive fuzzy controller which normally requires high gain signal. The learning control algorithm obviates any output derivative terms which are vulnerable to noise.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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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.

• 한국지능시스템학회논문지
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• 제10권5호
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• pp.466-477
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• 2000

이 논문에서는 운반차-막대 시스템을 제어하기 위한 CMAC을 이용한 적응 학습 제어계를 개발하기 위하여, 적응비평학습을 이용하는 신경망 제어계에 관한 여러 연구 문헌들을 조사하고, ASE 요소를 이용하는 적응비평학습 기법을 CMAC을 바탕으로 하는 제어계에 통합하였다. 적응비평학습 기법을 CMAC에 구현하는데 있어서의 변환 문제를 검토하고, CMAC 제어계와 ASE 제어계가 운반차-막대 문제를 학습하는 속도를 비교하여, CMAC 제어계의 학습 속도가 빠르기는 하지만, 입력 공간의 더 넓은 영역에 대해서는 학습효과를 발휘하지 못하는 문제의 관점에서 적응비평학습 방법이 CMAC의 특성과 어울리는지를 고찰하였다.

• 한국통신학회논문지
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• 제13권4호
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• pp.352-359
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• 1988

학습제어 시스템은 제어대상 시스템의 파라미터를 모르는 경우에 파라미터 적응의 개념을 도입해서, 일종의 hybrid형 적응제어 시스템으로 간주하여 설계될 수 있다. 이러한 파라미터 적응형 학습제어 시스템은 이미 보고되었으나 연속시간 시스템에만 적용될 수 있었다. 본 논문에서는 메모리소자를 반드시 포함하여야 하는 학습시스템에 대하여, 위의 제어알고리즘을 이산화 함으로써 디지탈기술의 발전에 비추어 실제의 적용을 용이하도록 하였으며, 그 타당성을 시뮬레이션으 통하여 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.316-321
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• 2003

This paper presents a design method of the wavelet neural network based controller using direct adaptive control method to deal with a stable intelligent control of chaotic systems. The various uncertainties, such as mechanical parametric variation, external disturbance, and unstructured uncertainty influence the control performance. However, the conventional control methods such as optimal control, adaptive control and robust control may not be feasible when an explicit, faithful mathematical model cannot be constructed. Therefore, an intelligent control system that is an on-line trained WNN controller based on direct adaptive control method with adaptive learning rates is proposed to control chaotic nonlinear systems whose mathematical models are not available. The adaptive learning rates are derived in the sense of discrete-type Lyapunov stability theorem, so that the convergence of the tracking error can be guaranteed in the closed-loop system. In the whole design process, the strict constrained conditions and prior knowledge of the controlled plant are not necessary due to the powerful learning ability of the proposed intelligent control system. The gradient-descent method is used for training a wavelet neural network controller of chaotic systems. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with application to the chaotic systems.

• International Journal of Precision Engineering and Manufacturing
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• 제7권1호
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• pp.62-66
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• 2006

The learning control develops controllers that learn to improve their performance at executing a given task, based on experience performing this specific task. In a previous work, the authors presented an iterative precision of linear decentralized learning control based on p-integrated learning method for the vertical dynamic multiple systems. This paper develops an indirect decentralized learning control based on adaptive control method. The original motivation of the learning control field was learning in robots doing repetitive tasks such as an assembly line works. This paper starts with decentralized discrete time systems, and progresses to the robot application, modeling the robot as a time varying linear system in the neighborhood of the nominal trajectory, and using the usual robot controllers that are decentralized, treating each link as if it is independent of any coupling with other links. Some techniques will show up in the numerical simulation for vertical dynamic robot. The methods of learning system are shown for the iterative precision of each link.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국제학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.401-405
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• 1993

This paper presents a neuro adaptive control method for nonlinear dynamical systems based on artificial neural network systems. The proposed neuro adaptive controller consists of 3 layers artificial neural network system and parallel PD controller. At the early stage in learning or identification process of the system characteristics the PD controller works mainly in order to compensate for the inadequacy of the learning process and then gradually the neuro contrller begins to work instead of the PD controller after the learning process has proceeded. From the simulation studies the neuro adaptive controller is seen to be robust and works effectively for nonlinear dynamical systems from a practical applicational points of view.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 추계학술대회 논문집
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• pp.396-400
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• 1996

It is prerequisite to apply dynamics controller to control robot manipulator required to perform fast and Precise motion. In this Paper, we Propose an adaptive 3earning control method for the dynamic control of a robot manipulator. The proposed control scheme is made up of PD controller in the feedback loop and the adaptive learning controller in the feedforward loop. 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 SCARA robot and experimentally verified.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 춘계학술대회 논문집
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• pp.262-266
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• 1995

This paper presents a new adaptive-neuro control scheme to control the velocity and position of SCARA robot with parameter uncertainties. The adaptive control of linear system found wiedly in many areas of control application. While techniques for the adaptive control of linear systems have been well-established in the literature, there are a few corresponding techniques for nonlinear systems. In this paper an attempt is made to present a newcontrol scheme for theadaptive control of ponlinear robot based on a feedforward neural network. The proposed approach incorporates a neuro controller used within a reinforcement learning framework, which reduces the problem to one of learning a stochastic approximation of an unknown average error surface Emphasis is focused on the fact that the adaptive-neuro controoler dose not need any input/output information about the controlled system. The simulation result illustrates the effectiveness of the proposed adaptive-neuro control scheme.