• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.751-755
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• 1997

This paper presents a new scheme of neural network controller to improve to improve the robustuous of robot manipulator using digital signal processors. Digital processors, DSPs, are micro-processors that are particularly developed for fast numerical computations involving sums and products of variables. Digital version of most advanced control algorithms can be defined as sums and producrs of measured variables, thus it can be programmed and executed through DSPs. In addition, DSPs are as fist in computation as most 32-bit micro-processors and yet at a fraction of their prices. These features make DSPs a viable computational tool in digital implementation of sophisticated controllers. During past decade it was proposed the well-established theorys for the adaptive control of linear systems, but there exits relativly little gensral theoral for the adaptive control of nonlinear systems. Perforating of the proposed controller is illustrated. This paper describes a new approach to the design of adaptive controller and implementation of real-time control for assembling robotic manipulator using digital signal processor. Digital signal processors used in implementing real time adaptive control algorithm are TMS320C50 series made in TI'Co..

• 제어로봇시스템학회논문지
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• 제8권12호
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• pp.1023-1030
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• 2002

Since the dynamics of autonomous underwater vehicles (AUVs) are highly nonlinear and their hydrodynamic coefficients vary with different vehicle's operating conditions, high performance control systems of AUVs are needed to have the capacities of teaming and adapting to the variations of the vehicle's dynamics. In this paper, a linearly parameterized neural network (LPNN) is used to approximate the uncertainties of the vehicle dynamics, where the basis function vector of the network is constructed according to the vehicle's physical properties. The network's reconstruction errors and the disturbances in the vehicle dynamics are assumed be bounded although the bound may be unknown. To attenuate this unknown bounded uncertainty, a certain estimation scheme for this unknown bound is introduced combined with a sliding mode scheme. The proposed controller is proven to guarantee that all signals in the closed-loop system are uniformly ultimately bounded (UUB). Numerical simulation studies are performed to illustrate the effectiveness of the proposed control scheme.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권11호
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• pp.476-482
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• 2006

Based on a field-oriented model of induction motor, an adaptive backstepping control approach using neural networks is proposed in this paper for the speed control of induction motors with uncertainties at a minimum of information. Neural networks are used to approximate most of uncertainties which are derived from unknown motor parameters, load torque disturbances and unknown nonlinearities and an adaptive backstepping controller is used to derive adaptive law of neural networks and control input directly. The controller is implemented by the hardware using DSP and the effectiveness of the proposed approach is verified by carrying out the experiment.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.1021-1024
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• 1999

In this paper, we propose a modified feedback neural network structure for adaptive control of robot manipulators. The proposed structure is that all of network output feedback into hidden units and output units. Learning algorithm is standard back-propagation algorithm. The simulation showed the effectiveness of using the new neural network structure in the adaptive control of robot manipulators.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2001년도 춘계종합학술대회
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• pp.456-459
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• 2001

본 논문에서는 웨이브렛 신경회로망을 이용한 적응 제어 방식을 제안한다. 웨이브렛 신경망의 구조는 은닉층의 시그모이드 함수를 mother 웨이브렛 함수로 대치한 것을 제외하고는 다층 신경회로망 구조와 비슷하다. 단일 관절 매니률레이터를 대상으로 적응 제어 시뮬레이션을 수행한 결과 웨이브렛 신경회로망의 우수성을 확인하였다.

• 제어로봇시스템학회논문지
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• 제14권12호
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• pp.1218-1225
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• 2008

This paper presents the design of a neural network based adaptive control for missile is presented. The application model is Exocet MM40, which is derived from missile DATCOM database. Acceleration of missile by tail Fin control cannot be controllable by DMI (Dynamic Model Inversion) directly because it is non-minimum phase system. So, the inner loop consists of DMI and NN (Neural Network) and the outer loop consists of PI controller. In order to satisfy the performances only with PI controller, it is necessary to do some additional process such as gain tuning and scheduling. In this paper, all flight area would be covered by just one PI gains without tuning and scheduling by applying mixture control technique of conventional controller and NN to the outer loop. Also, the simulation model is designed by considering non-minimum phase system and compared the performances to distinguish the validity of control law with conventional PI controller.

• 조명전기설비학회논문지
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• 제21권7호
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• pp.103-108
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• 2007

신경회로망은 많은 제어 시스템 분야에서 이용되고 있으나, 단일 궤환 신경회로망 제어기로 사용할 경우 입출력 패턴을 구하기 쉽지 않고, 부하급변 및 외란이 인가되는 경우에는 만족할만한 성능을 얻을 수 없었다. 이러한 문제를 해결하기 위해 본 논문에서는 신경회로망 출력노드의 활성화 함수 대신에 제어 대상체를 사용하는 새로운 알고리즘을 제안하였다. 결과적으로 제안된 신경회로망 자율 적응 제어 시스템은 구조가 간략화 되었으며 입출력 패턴의 문제가 해결되었고 일반적인 역전파 알고리즘을 이용하여 실시간으로 학습이 가능하게 되었다. 제안된 신경망 자율 적응 제어의 알고리즘 효과는 고속연산을 실행하는 DSP(TMS320C32)에 알고리즘을 탑재하여 A.C. 서보 모터의 속도제어에 의해서 확인하였다.

• International Journal of Control, Automation, and Systems
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• 제6권3호
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• pp.453-459
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• 2008

The neural network is currently being used throughout numerous control system fields. However, it is not easy to obtain an input-output pattern when the neural network is used for the system of a single feedback controller and it is difficult to obtain satisfactory performance with when the load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object for control and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The real plant object for controlling of this mode implements a simple neural network controller replacing the activation function and provides the error back propagation path to calculate the error at the output node. As the controller is designed using a simple structure neural network, the input-output pattern problem is solved naturally and real-time learning becomes possible through the general error back propagation algorithm. The new algorithm applied neural network controller gives excellent performance for initial and tracking response and shows a robust performance for rapid load change and disturbance, in which the permissible error surpasses the range border. The effect of the proposed control algorithm was verified in a test that controlled the speed of a motor equipped with a high speed computing capable DSP on which the proposed algorithm was loaded.

• 전자공학회논문지A
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• 제30A권4호
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• pp.37-43
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• 1993

As an approach to develope the intelligent control scheme, this paper will propose an adaptive neural-fuzzy control scheme. The proposed neural-fuzzy control system, which consists of the Fuzzy-Neural Controller(FNC) and Model Neural Network(MNN), has two important characteristics of adaptation and learning. The error back propagation algorithm has been adopted as a learning technique.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 1999년도 춘계종합학술대회
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• pp.370-374
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• 1999

본 논문에서는 역전파 알고리즘과 헵 학습규칙의 장점을 최대한 살려 이용하고, 역전파 알고리즘의 문제점인 지역 최소점에 빠지는 경우와 학습시간이 느린 단점과 헵 학습규칙의 문제점인 학습 패턴의 저장능력이 매우 제한되고 선형적 분리가 되지 않는 복잡한 문제에는 적용할 수 없다는 단점등을 개선하기 위하여 혼합형 학습규칙을 제안한다. 제안하는 학습규칙은 입력층과 은닉층에 흔합형 학습규칙과 은닉층과 출력층에 역전파(Back-Propagation) 학습규칙을 적용한 혼합형이다. 제안한 혼합형 학습규칙을 이용한 신경회로망의 유용성을 확인하기 위하여 단일관절 매니플레이터를 이용하여 추종제어에 대한 시뮬레이션을 하여 기존의 역전파 알고리즘을 이용한 직접적응 제어 방식과 제어성능을 비교 검토한 결과 다음과 같은 특성을 확인하였다.