• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
• /
• pp.6-8
• /
• 2005

In this paper, we discuss model identification of nonlinear data using GAs-based Fuzzy Polynomial Neural Networks(GAs-FPNN). Fuzzy Polynomial Neural Networks(FPNN) is proposed model based Group Method Data Handling(GMDH) and Neural Networks(NNs). Each node of FPNN is expressed Fuzzy Polynomial Neuron(FPN). Network structure of nonlinear data is created using Genetic Algorithms(GAs) of optimal search method. Accordingly, GAs-FPNN have more inflexible than the existing models (in)from structure selecting. The proposed model select and identify its for optimal search of Genetic Algorithms that are no. of input variables, input variable numbers and consequence structures. The GAs-FPNN model is select tuning to input variable number, number of input variable and the last part structure through optimal search of Genetic Algorithms. It is shown that nonlinear data model design using Genetic Algorithms based FPNN is more usefulness and effectiveness than the existing models.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2002년도 ICCAS
• /
• pp.56.5-56
• /
• 2002

This paper studies on the feedback linearization of the looper system for hot strip mills, where the looper system plays an important role in regulating the strip tension. Firstly, nonlinear dynamic equations of the looper system are simply introduced. Secondly, using the static feedback linearization algorithm, a linear model of the looper system is obtained, of which usefulness is validated from comparison between the linear model and the nonlinear model, and design of LQI(Linear Ouadratic Integral optimal control) and ILQ (Inverse Linear Quadratic optimal control) looper control systems. In result, it is shown that the linear looper model by the feedback linearization well describes nonlin...

• 품질경영학회지
• /
• 제24권4호
• /
• pp.59-69
• /
• 1996

The criteria for construction of D-optimal design in nonlinear models are derived. The procedures for finding these optimal designs using the simulated annealing algorithm are presented. It is claimed that the advantages of this method are its ability to make the given data useful by adding new observations as well as to obtain a new set of appropriate data when the model and parameters are known. Research so far indicates that there has never been a case in which there is more than one independent variable but this method can be used for such cases. The result indicates the effectiveness of the method using simulated annealing algorithm for situations in which there are many independent variables and the ranges of design spaces are wide.

• 대한기계학회논문집A
• /
• 제30권10호
• /
• pp.1209-1218
• /
• 2006

Precision servomechanisms are widely used in machine tool, semiconductor and flat panel display industries. It is important to improve contouring accuracy in high-precision servomechanisms. In order to improve the contouring accuracy, cross-coupled control systems have been proposed. However, it is very difficult to select the controller parameters because cross-coupled control systems are multivariable, nonlinear and time-varying systems. In this paper, in order to improve contouring accuracy of a biaxial servomechanism, a cross-coupled controller is adopted and an optimal tuning procedure based on an integrated design concept is proposed. Strict mathematical modeling and identification process of a servomechanism are performed. An optimal tuning problem is formulated as a nonlinear constrained optimization problem including the relevant controller parameters of the servomechanism. The objective of the optimal tuning procedure is to minimize both the contour error and the settling time while satisfying constraints such as the relative stability and maximum overshoot conditions, etc. The effectiveness of the proposed optimal tuning procedure is verified through experiments.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2001년도 ICCAS
• /
• pp.71.2-71
• /
• 2001

This paper deals with nonlinear system modelling using neural network and genetic algorithm. Application of neural network to control and identification is actively studied because of their approximating ability of nonlinear function. It is important to design the neural network with optimal structure for minimum error and fast response time. Genetic algorithm is getting more popular nowadays because of their simplicity and robustness. In this paper, We optimize neural network structure using genetic algorithm. The genetic algorithm uses binary coding for neural network structure and search for optimal neural network structure of minimum error and response time. Through extensive simulation, Optimal neural network structure is shown to be effective for ...

• 한국수학교육학회지시리즈B:순수및응용수학
• /
• 제2권2호
• /
• pp.133-140
• /
• 1995

The D-optimal design criterion for precise parameter estimation in nonlinear regression analysis is called the determinant criterion because the determinant of a matrix is to be maximized. In this thesis, we derive the gradient and the Hessian of the determinant criterion, and apply a QR decomposition for their efficient computations. We also propose an approximate form of the Hessian matrix which can be calculated from the first derivative of a model function with respect to the design variables. These equations can be used in a Gauss-Newton type iteration procedure.

• 제어로봇시스템학회논문지
• /
• 제20권3호
• /
• pp.261-269
• /
• 2014

This paper provides an overview of the basics and recent studies of Lyapunov-based nonlinear adaptive control, the aim of which is to improve or maintain the performance and stability of the closed-loop system by cancelling out the presumable uncertainties in the nonlinear system dynamics. The design principles are essentially based on Lyapunov's direct method. In this survey, we provide a comprehensive overview of Lyapunov-based nonlinear adaptive control techniques with simplified effective design examples, which are to be elaborated as related recent results are gradually shown. The scope of the survey contains research on singularity problems in adaptive control, the techniques to deal with linearly and nonlinearly parameterized uncertainties, robust neuro-adaptive control, and adaptive control methodologies combined with various nonlinear control techniques such as sliding-mode control, back-stepping, dynamic surface control, and optimal/$H_{\infty}$ control.

• Journal of Astronomy and Space Sciences
• /
• 제21권3호
• /
• pp.209-220
• /
• 2004

Some methods have been presented to get optimal formation trajectories in the step of configuration or reconfiguration, which subject to constraints of collision avoidance and final configuration. In this study, a method for optimal formation trajectory-planning is introduced in view of fuel/time minimization using parameter optimization technique which has not been applied to optimal trajectory-planning for satellite formation flying. New constraints of nonlinear equality are derived for final configuration and constraints of nonlinear inequality are used for collision avoidance. The final configuration constraints are that three or more satellites should be placed in an equilateral polygon of the circular horizontal plane orbit. Several examples are given to get optimal trajectories based on the parameter optimization problem which subjects to constraints of collision avoidance and final configuration. They show that the introduced method for trajectory-planning is well suited to trajectory design problems of formation flying missions.

• Smart Structures and Systems
• /
• 제23권3호
• /
• pp.307-318
• /
• 2019

The frequently excessive vibrations presented in civil structures during seismic events or service conditions may result in users' discomfort, or worst, in structures failure, producing economic and even human casualties. This work contributes in proposing the synthesis of a nonlinear optimal control strategy for semiactive structural control, with the main characteristic that the synthesis considers both the structure model and the semiactive actuator nonlinear dynamics, which produces a nonlinear system that requires a nonlinear controller design. The aim is to reduce the unwanted vibrations in the response of civil structures, by means of intelligent fluid semiactive actuator such as the Magnetorheological Damper (MRD), which is a device with a low level of power consumption. The civil structures for which the proposed control methodology can be applied are those admitting a state-dependent coefficient factorized representation model, such as buildings, bridges, among others. A scaled model of a three storey building is analyzed as a case study, whose dynamical response involves displacement, velocity and acceleration of each one of the storeys, subjected to the North-South component of the September 19th., 2017, Puebla-Morelos (7.1M), Mexico earthquake. The investigation rests on comparing the structural response over time for two different conditions: with no control device installed and with one MRD installed between the first floor and the ground, where a nonlinear optimal signal for the MRD input voltage is determined. Simulation results are presented to show the effectiveness of the proposed controller for reducing the building's dynamical response.

• 한국지진공학회논문집
• /
• 제11권6호
• /
• pp.1-14
• /
• 2007

인접 구조물의 지진응답 제어를 위한 비선형 감쇠시스템의 최적 설계 방법에 관하여 연구하였다. 최적 설계를 위한 목적 함수로는 구조물의 응답과 감쇠기의 총 사용량을 고려하였으며, 상충하는 두 목적함수를 합리적인 수준에서 동시에 최소화하는 해를 구하기 위하여 유전자 알고리즘에 기반한 다목적 최적화 방법을 도입하였다. 또한, 최적화 과정에서 요구되는 비선형 시간이력해석을 수행하지 않고도, 비선형 이력감쇠기로 연결된 구조물의 지진응답을 효율적으로 평가하기 위하여 추계학적 선형화 방법을 접목하였다. 제시하는 방법의 효율성을 검증하기 위한 수치 예로서 20층과 10층의 인접 빌딩을 고려하였으며, 두 빌딩을 연결하는 비선형 감쇠시스템으로는 입력전압의 크기에 따라 변화하는 감쇠성능을 보이는 MR 감쇠기를 도입하였다. 제시하는 방법을 통하여 MR 감쇠기의 각 층별 최적 개수 및 감쇠용량을 결정할 수 있었으며, 이는 일반적인 균등분포 시스템에 비해 유사한 제어성능을 보이면서도 훨씬 경제적이었다. 또한, 인접구조물간 충돌에 대하여도 확률적으로 안정적인 거동을 보임을 검증하였으며, 제시하는 방법이 준능동 제어시스템의 최적 배치를 결정하기 위한 설계문제에도 적용할 수 있음을 보였다.