• 제어로봇시스템학회논문지
• /
• 제6권9호
• /
• pp.739-744
• /
• 2000

A robust control algorithm under disturbance and reference change is developed using a self tuning control method incorporting of the well known internal model principle and the annihilator polynomical. The types of disturbance and reference signal are assumed to be given as known difference polynomials. The algorithm is shown for a minimum phase system with parameters of unknown parameters.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
• /
• pp.499-501
• /
• 1998

In this paper, we present a self-compensating PID controller which consists of a conventional PID controller that controls the linear components and a neural controller that controls the higher order and nonlinear components. This controller is based on the Harris's concept where he explained that the adaptive controller consists of the PID control term and the disturbance compensating term. The resulting controller's architecture is also found to be very similar to that of Wang's controller. This controller adds a self-tuning ability to the existing PID controller without replacing it by compensating the control errors through the neuro-controller. When applied to an actual magnetic levitation system which is known to be very nonlinear, it has also produced an excellent results.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2001년도 ICCAS
• /
• pp.29.1-29
• /
• 2001

Neuro-fuzzy systems are multi-layered connectionist networks that realize the elements and functions of traditional fuzzy logic control/decision systems. A trained neuro-fuzzy system is isomorphic to a fuzzy logic system, and fuzzy IF-THEN rule knowledge can be explicitly extracted from the network. This talk presents a brief introduction to self-adaptive neuro-fuzzy systems and addresses some recent research results and applications. Most of the existing neuro-fuzzy systems exhibit several major drawbacks that lead to performance degradation. These drawbacks are the curse of dimensionality (i.e., fuzzy rule explosion), inability to re-structure their internal nodes in a changing environment, and their lack of ability to extract knowledge from a given set of training data. This talk focuses on our investigation of network architectures, self-adaptation algorithms, and efficient learning algorithms that will enable existing neuro-fuzzy systems to self-adapt themselves in an unstructured and uncertain environment.

• 대한전기학회논문지
• /
• 제36권5호
• /
• pp.367-376
• /
• 1987

In this paper we have disigned and implemented a real time ALE (adaptive line enhancer) using a high speed digital processor,NEC 7720. For the ALE system, we have employed an adaptive LMS(least mean square) algorithm proposed by Widrow and Hoff and a 32-order FIR(finite impulse response) filter. Extensive computer simulations have been performed to investigate the peformance of the ALE and to determine necessary parameters for hardware design. The developed software for an NEC 7720 was tested in real time operation using an NEC7720 hardware emulator. The ALE has been tested by sinusoidal waves and real CW (continuous wave) signals. It was found that the experimental results were well agreed with the computer simulation results. Thus it can be concluded that the ALE is useful for detection and enhancement of a sinusoidal signal which is corrupted by an additive Gaussian noise.

• 한국통신학회논문지
• /
• 제39A권1호
• /
• pp.28-35
• /
• 2014

본 논문에서는 상대국과 자국간의 채널 상황이 악화될 경우에도 통신의 신뢰도를 보장하고 동일 대역에서 동시에 전 이중 통신을 하기 위한 터보 등화기를 결합한 SSD(simultaneous single band duplex)시스템을 제안한다. 본 논문에서 제안하는 시스템은 동일 대역 동시 통신에 의해서 발생하는 자기 간섭 신호를 제거하기 위한 방법으로 RF(radio frequency) Cancellation과 Digital Cancellation을 사용하며 자기 간섭 신호를 효과적으로 제거 한 뒤 터보 등화기를 이용하여 상대국과 자국간의 열악한 채널에 의하여 발생한 신호의 왜곡을 등화 한다. 본 논문에서는 제안하는 터보 등화기를 결합한 SSD 시스템을 설계하고 적응 등화기를 사용한 SSD 시스템과 성능을 비교 분석하였다. 본 논문에서는 제안하는 시스템을 Simulink 프로그램을 이용하여 구성하고 성능을 확인하였다. 본 논문에서 제안하는 터보 등화기를 결합한 SSD 시스템의 성능을 확인한 결과 적응 등화기를 사용하는 SSD 시스템에 비하여 상대국과 자국간의 채널 상황이 열악한 상황에서 ISI(inter symbol interference)에 의한 신호의 왜곡을 보다 효과적으로 등화하고 자기 간섭 신호를 제거하면서 동일 대역에서 동시에 전 이중 통신을 할 수 있는 것을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
• /
• pp.243-246
• /
• 1994

In this paper, a self-learning fuzzy controller is designed with a fuzzy approximation of an inverse model. The aim of an identification is to find an input command which is control of a system output. It is intuitional and easy to use a classical adaptive inverse modeling method for the identification, but it is difficult and complex to implement it. This problem can be solved with a fuzzy approximation of an inverse modeling. The fuzzy logic effectively represents the complex phenomena of the real world. Also fuzzy system could be represented by the neural network that is useful for a learning structure. The rule of a fuzzy inverse model is modified by the gradient descent method. The goal is to be obtained that makes the design of fuzzy controller less complex, and then this self-learning fuzz controller can be used for nonlinear dynamic system. We have applied this scheme to a nonlinear Ball and Beam system.

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

• 제어로봇시스템학회논문지
• /
• 제8권2호
• /
• pp.126-135
• /
• 2002

The study is concerned with an approach to the design of new architectures of fuzzy neural networks and the discussion of comprehensive design methodology supporting their development. We propose an Adaptive Fuzzy Polynomial Neural Networks(APFNN) based on Fuzzy Neural Networks(FNN) and Self-organizing Networks(SON) for model identification of complex and nonlinear systems. The proposed AFPNN is generated from the mutually combined structure of both FNN and SON. The one and the other are considered as the premise and the consequence part of AFPNN, respectively. As the premise structure of AFPNN, FNN uses both the simplified fuzzy inference and error back-propagation teaming rule. The parameters of FNN are refined(optimized) using genetic algorithms(GAs). As the consequence structure of AFPNN, SON is realized by a polynomial type of mapping(linear, quadratic and modified quadratic) between input and output variables. In this study, we introduce two kinds of AFPNN architectures, namely the basic and the modified one. The basic and the modified architectures depend on the number of input variables and the order of polynomial in each layer of consequence structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the AFPNN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed AFPNN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• International Journal of Control, Automation, and Systems
• /
• 제4권6호
• /
• pp.756-762
• /
• 2006

Shape Memory Alloy(SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications such as aeronautics, surgical tools, robotics and so on. Although the conventional PID controller can be used with slow response systems, there has been limited success in precise motion control of SMA actuators, since the systems are disturbed by unknown factors beside their inherent nonlinear hysteresis and changes in the surrounding environment of the systems. This paper presents a new development of a SMA position control system by using a self-tuning fuzzy PID controller. This control algorithm is used by tuning the parameters of the PID controller thereby integrating fuzzy inference and producing a fuzzy adaptive PID controller, which can then be used to improve the control performance of nonlinear systems. The experimental results of position control of SMA actuators using conventional and self-tuning fuzzy PID controllers are both included in this paper.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
• /
• pp.704-707
• /
• 1997

In this paper, we present a fuzzy logic based tuner for continuous on-line tuning of PID controllers. The essential idea of the scheme is to parameterize a Ziegler-Nichols-like tuning formula by a singler parameter .alpha., then to use an on line fuzzy logic to self-tune the parameter. The adaptive scaling makes the controller robust against large variations in parametric and dynamics uncertainties in the plant model. New self-tuning controller has the ability to decide when to use PI or PID control by extracting process dynamics from relay experiments. These scheme lead to improved performance of the transient and steady state behavior of the closed loop system, including processes with nonminimum phase processes.