• Title/Summary/Keyword: 비선형 적응제어

Search Result 288, Processing Time 0.022 seconds

A Design Technique for Stabilization of Inverted Pendulum Cart System on the Inclined Rail (경사 레일상에 있는 도립진자 장치의안정화 설계기법)

  • 박영식;최부귀;윤병도
    • The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
    • /
    • v.3 no.4
    • /
    • pp.62-69
    • /
    • 1989
  • 휴대용 전기톱을 비롯한 학습 기계장치, 자동차 연동장치, 각종 화학 분석장치 및 산업용 로봇 시스템등의 전기설비에 광범위하게 응용되고 있는 고유 불안정 도립진자 시스템의 동적 안정화 제어기 설계기법이 소개된다. 복잡한 비선형 동특성을 고려한 수학적 모델링과 C. D. Johnson에 의해 제시된 외란 적응 제어 이론을 적응하여, 최적 레귤레이터형 안정화 제어기를 설계하였으며, 컴퓨터 시뮬레이션 및 실험결과가 만족스럽게 나타났다.

  • PDF

Power system stabilization via adaptive feedback linearization (비선형 적응제어를 이용한 전력계통 안정화)

  • 윤태웅;이도관
    • 제어로봇시스템학회:학술대회논문집
    • /
    • 1996.10b
    • /
    • pp.1221-1224
    • /
    • 1996
  • As in most industrial processes, the dynamic characteristics of an electric power system are subject to changes. Amongst those effects which cause the system to be uncertain, faults on transmission lines are considered. For the stabilization of the power system, we present an indirect adaptive control method, which is capable of tracking a sudden change in the effective reactance of a transmission line. As the plant dynamics are nonlinear, an input-output feedback linearization method is combined with an identification algorithm which estimates the effect of a fault.

  • PDF

Fault Tolerant Controller Design for Supersonic Advanced Trainer Using Model Following Adaptive Technique (모델추종 적응제어기법을 이용한 초음속 고등훈련기의 고장허용제어기 설계)

  • Kim, Seung-Keun;Lee, Ho-Jin;Yoon, Seung-Ho;Han, Young-Su;Kim, You-Dan;Kim, Chong-Shup;Cho, In-Je
    • Journal of the Korean Society for Aeronautical & Space Sciences
    • /
    • v.37 no.5
    • /
    • pp.464-469
    • /
    • 2009
  • In this study, a new fault tolerant controller based on a model following adaptive technique is applied to the reconfiguration mode of supersonic advanced trainer. The designed controller is applied to the flight control system of high performance aircraft. To verify the performance of the proposed controller, numerical simulations are executed using a non-realtime nonlinear verification tool.

(Design of Neural Network Controller for Contiunous-Time Chaotic Nonlinear Systems) (연속 시간 혼돈 비선형 시스템을 위한 신경 회로망 제어기의 설계)

  • O, Gi-Hun;Choe, Yun-Ho;Park, Jin-Bae;Im, Gye-Yeong
    • Journal of the Institute of Electronics Engineers of Korea SC
    • /
    • v.39 no.1
    • /
    • pp.51-65
    • /
    • 2002
  • This paper presents a design method of the neural network-based controller using an indirect adaptive control method to deal with an intelligent control for chaotic nonlinear systems. The proposed control method includes the identification and control Process for chaotic nonlinear systems. The identification process for chaotic nonlinear systems is an off-line process which utilizes the serial-parallel structure of multilayer neural networks and simple state space neural networks. The control process is an on-line process which uses the trained neural networks as the system model. An error back-propagation method was used for training of identification and control for chaotic nonlinear systems. The performance of the proposed neural network controller was evaluated by application to the Duffing equation and the Lorenz equation, and the proposed controller was compared with other neural network-based controllers by computer simulations.

Adaptive Neural Control of Nonlinear Pure-feedback Systems (완전궤환 비선형 계통에 대한 적응 신경망 제어기)

  • Park, Jang-Hyun;Kim, Seong-Hwan;Chang, Young-Hak
    • Journal of IKEEE
    • /
    • v.14 no.3
    • /
    • pp.182-189
    • /
    • 2010
  • A new Adaptive neural state-feedback controller for the fully nonaffine pure-feedback nonlinear system are presented in this paper. By reformulating the original pure-feedback system to a standard normal form with respect to newly defined state variables, the proposed controller requires no backstepping design procedure. Avoiding backstepping makes the controller structure and stability analysis considerably simple. The proposed controller employs only one neural network to approximate unknown ideal controllers, which highlights the simplicity of the proposed neural controller. Simulation examples demonstrate the efficiency and performance of the proposed approach.

Nonlinear Adaptive Control for Linear Motor through the Estimation of Friction Forces and Force Ripples (마찰력 및 리플력 추정을 통한 리니어 모터의 비선형 적응제어)

  • Kim, Hong-Bin;Lee, Byong-Huee;Han, Sang-Oh;Huh, Kun-Soo
    • Transactions of the Korean Society of Mechanical Engineers A
    • /
    • v.31 no.1 s.256
    • /
    • pp.18-25
    • /
    • 2007
  • Linear motor is easily affected by load disturbance, force ripple, friction, and parameter variations because there is no mechanical transmission to reduce the effects of model uncertainties and external disturbance. These nonlinear effects have been reduced for high-speed/high-accuracy position control either through the better motor design or via the better control algorithm that can compensate the nonlinear effects. In this paper, a nonlinear adaptive control algorithm is designed and applied for the position control of permanent magnet linear synchronous motor. In order to estimate and compensate the nonlinear effects such as friction and force ripple, the estimation and the nonlinear adaptive control laws are derived based on the virtual control input and a suitable Lyapunov function. The proposed controller is evaluated through the computer simulations. The control algorithm is also implemented to a DSP board and interfaced to the PMLSM for verifying the realtime control performance.

Development of Robust Adaptive Learning Control for Nonlinear System (비선형 시스템에 대한 강인성 적응 학습 제어기의 개발)

  • Yu, Yeong-Sun;Ha, Hwan-Su
    • Transactions of the Korean Society of Mechanical Engineers A
    • /
    • v.25 no.12
    • /
    • pp.1895-1902
    • /
    • 2001
  • This paper gives an overview of the relationships between methods of loaming and adaptive control. It is the objective of this paper to develop adaptive learning control algorithms that combine the advantages of adaptive control with those of leaning control to the extent possible for the type of system model used. The robustness of this adaptive loaming control with respect to reinitialization errors and fluctuation of dynamics from disturbance is analyzed extensively. Simulation results have shown to verify the effectiveness of the proposed control algorithm.

Indirect Adaptive Control of Nonlinear Systems Using a EKF Learning Algorithm Based Wavelet Neural Network (확장 칼만 필터 학습 방법 기반 웨이블릿 신경 회로망을 이용한 비선형 시스템의 간접 적응 제어)

  • Kim Kyoung-Joo;Choi Yoon Ho;Park Jin Bae
    • Journal of the Korean Institute of Intelligent Systems
    • /
    • v.15 no.6
    • /
    • pp.720-729
    • /
    • 2005
  • In this paper, we design the indirect adaptive controller using Wavelet Neural Network(WNN) for unknown nonlinear systems. The proposed indirect adaptive controller using WNN consists of identification model and controller. Here, the WNN is used in both Identification model and controller The WNN has advantage of indicating the location in both time and frequency simultaneously, and has faster convergence than MLPN and RBFN. There are several training methods for WNN, such as GD, GA, DNA, etc. In this paper, we present the Extended Kalman Filter(EKF) based training method. Although it is computationally complex, this algorithm updates parameters consistent with previous data and usually converges in a few iterations. Finally, ore illustrate the effectiveness of our method through computer simulations for the Buffing system and the one-link rigid robot manipulator. From the simulation results, we show that the indirect adaptive controller using the EKF method has better performance than the GD method.

Design of a Self-tuning Controller with a PID Structure Using Neural Network (신경회로망을 이용한 PID구조를 갖는 자기동조제어기의 설계)

  • Cho, Won-Chul;Jeong, In-Gab;Shim, Tae-Eun
    • Journal of the Institute of Electronics Engineers of Korea SC
    • /
    • v.39 no.6
    • /
    • pp.1-8
    • /
    • 2002
  • This paper presents a generalized minimum-variance self-tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior and time delays. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation is done to adapt the nonlinear nonminimum phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct adaptive controller using neural network.

On Learning and Structure of Cerebellum Model Linear Associator Network(I) -Analysis & Development of Learning Algorithm- (소뇌모델 선형조합 신경망의 구조 및 학습기능 연구(I) -분석 및 학습 알고리즘 개발-)

  • Hwang, H.;Baek, P.K.
    • Journal of Biosystems Engineering
    • /
    • v.15 no.3
    • /
    • pp.186-198
    • /
    • 1990
  • 인간 소뇌의 구조와 기능을 간략하게 수학적으로 모델링하여 입력에 따른 시스템의 적정 출력을 학습에 의한 적응 제어 방식으로 추출해 내는 소뇌모델 대수제어기(CMAC : Cerebellar Model Arithmetic Controller)가 제안되었다. 본 논문에서는 연구개발된 기존 신경회로망과의 비교 분석에 의거하여, 소뇌모델 대수제어기 대신 네트의 특성에 따라 소뇌모델 선형조합 신경망(CMLAN : Cerebellum Model Linear Associator Network)이라 하였다. 소뇌모델 선형조합 신경망은 시스템의 제어 함수치를 결정하는 데 있어, 기존의 제어방식이 시스템의 모델링을 기초로 하여 알고리즘에 의한 수치해석적 또는 분석적 기법으로 모델 해를 산출하는 것과 달리, 학습을 통하여 저장되는 분산기억 소자들의 함수치를 선형적으로 조합함으로써 시스템의 입출력을 결정한다. 분산기억 소자로의 함수치 산정 및 저장은 소뇌모델 선형조합 신경망이 갖는 고유의 구조적 상태공간 매핑(State Space Mapping)과 델타규칙(Delta Rule)에 의거한 시스템의 입출력 상태함수의 학습으로써 수행된다. 본 논문을 통하여 소뇌모델 선형조합신경망의 구조적 특성, 학습 성질과 상태공간 설정 및 시스템의 수렴성을 규명하였다. 또한 기존의 최대 편차수정 학습 알고리즘이 갖는 비능률성 및 적용 제한성을 극복한 효율적 학습 알고리즘들을 제시하였다. 언급한 신경망의 특성 및 제안된 학습 알고리즘들의 능률성을 다양한 학습이득(Learning Gain)하에서 비선형 함수를 컴퓨터로 모의 시험하여 예시하였다.

  • PDF