• 한국소음진동공학회논문집
• /
• 제15권7호
• /
• pp.813-820
• /
• 2005

A deployable missile control fin has some structural nonlinearities because of the worn or loose hinges and the manufacturing tolerance. The structural nonlinearity cannot be eliminated completely, and exerts significant effects on the static and dynamic characteristics of the control fin. Thus, It is important to establish the accurate deployable missile control fin model. In the present study, the nonlinear dynamic model of 4he deployable missile control fin is developed using a substructure synthesis method. The deployable missile control fin can be subdivided Into two substructures represented by linear dynamic models and a nonlinear hinge with structural nonlinearities. The nonlinear hinge model is established by using a system identification method, and the substructure modes are improved using the Frequency Response Method. A substructure synthesis method Is expanded to couple the substructure models and the nonlinear hinge model, and the nonlinear dynamic model of the fin is developed. Finally, the established nonlinear dynamic model of the deployable missile control fin is verified by dynamic tests. The established model is In good agreement with test results, showing that the present approach is useful in aeroelastic stability analyses such as time-domain nonlinear flutter analysis.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
• /
• pp.453-456
• /
• 1996

In this paper, the direct adaptive control using neural networks is presented for the control of chaotic nonlinear systems. The direct adaptive control method has an advantage that the additional system identification procedure is not necessary. In order to evaluate the performance of our controller design method, two direct adaptive control methods are applied to a Duffing's equation and a Lorenz equation which are continuous-time chaotic systems. Our simulation results show the effectiveness of the controllers.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
• /
• pp.1253-1256
• /
• 1996

In this paper, a new nonlinear feedback linearization control scheme for induction motors is developed. The control scheme employs a fuzzy nonlinear identification scheme based on fuzzy basis function expansion to adoptively compensate the parameter variations, i.e. rotor resistance, mutual and self inductance etc. An important feature of the proposed control scheme is to incorporate the sliding mode controller into the scheme to speed up convergence rate. Simulation tests show the robust behavior of the proposed controller in the presence of the parameter uncertainties of the machine.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
• /
• pp.1133-1138
• /
• 1991

A control of nonlinear system is motivated by the fact that all real plants are nonlinear systems and model identification introduces parameter errors. The purpose of this study is to design a Discrete Variable Structure Controller(DVSC) for single-rod hydraulic cylinder system. The model contains uncertain parameters which we known to lie upper and lower bounds. In the design of DVSC, the boundary layer concept was adopted to reduce cattering. The DVSC was evaluated through digital computer simulation and compared with a VSC (analog controller).

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
• /
• pp.56-60
• /
• 1992

This paper addresses the problem of designing a neural network based controller for a discrete-time nonlinear dynamical system. Using two multi-layered neural networks we first design an indirect controller the weights of which are updated by the informations obtained from system identification. The weight update is executed by parameter optimization method under Lagrangian formulation. For the nonlinear dynamical system, we define several cost functions and by computer simulations analyze the control performances of them and the effects of penalty-weighting values.

• 전력전자학회:학술대회논문집
• /
• 전력전자학회 1998년도 전력전자학술대회 논문집
• /
• pp.239-244
• /
• 1998

We propose a nonlinear feedback controller that can control the induction motors with high dynamic performance by means of decoupling of motor speed and rotor flux. A new recursive adaptation algorithm for rotor resistance which can be applied to our nonlinear feedback controller is also presented in this paper. Some simulation results show that the adaptation algorithm for rotor resistance is robust against the variation of stator resistance and mutual inductance. In addition, it is computationally simple and has small estimation errors.

• 한국전산구조공학회:학술대회논문집
• /
• 한국전산구조공학회 2011년도 정기 학술대회
• /
• pp.278-281
• /
• 2011

The global coordinates generated from Isomap algorithm provide a simple way to analyze and manipulate high dimensional observations in terms of their intrinsic nonlinear degrees of freedom. Thus, Isomap can find globally meaningful coordinates and nonlinear structure of complex data sets, while neither principal component analysis (PCA) nor multidimensional scaling (MDS) are successful in many cases. It is demonstrated that the adapted Isomap algorithm successfully enhances the quality of pattern classification for damage identification in various numerical examples.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2001년도 춘계학술대회 학술발표 논문집
• /
• pp.191-194
• /
• 2001

The ozone forecasting systems have many problems because the mechanism of the ozone concentration is highly complex, nonlinear, and nonstationary. Also, the results of prediction are not a good performance so far, especially in the high-level ozone concentration. This paper describes the modeling method of the ozone prediction system using neuro-fuzzy approaches and fuzzy clustering. The dynamic polynomial neural network (DPNN) based upon a typical algorithm of GMDH (group method of data handling) is a useful method for data analysis, identification of nonlinear complex system, and prediction of a dynamical system.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1995년도 추계학술대회 학술발표 논문집
• /
• pp.249-257
• /
• 1995

In this paper, we demeonstrate that neural networks can be used effectively for the control of nonlinear dynamical system. To adaptively control a plant, there are two distinct approach. these are direct control and indirect control. Both direct and Indirect adaptive control are trained using static back propagation. In indirect, using the resulting identification model, which contains neural networks and linear dynamical elements as subsystems, the parameters of the controller are adjusted.

• Journal of Electrical Engineering and Technology
• /
• 제11권2호
• /
• pp.265-273
• /
• 2016

A water wall system is one of the most important components of a boiler in a thermal power plant, and it is a nonlinear Multi-Input and Multi-Output (MIMO) system, with 6 inputs and 3 outputs. Three models are developed and comp for the controller design, including a linear model, a multilayer feed-forward neural network (MFNN) model and an Echo State Network (ESN) model. First, the linear model is developed by linearizing a given nonlinear model and is analyzed as a function of the operating point. Second, the MFNN and the ESN are developed by using training data from the nonlinear model. The three models are validated using Matlab with nonlinear input-output data that was not used during training.