• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.82-85
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• 1987

The objective of this paper is to develop computer programs to aid in the design and analysis of control systems in which nonlinear characteristics exist. Control systems are dynamic systems, which can be described using various mathematical models. A convenient model for digital computer simulation is the state model in which described using a set of linear and non linear first order differential equations. The digital simulation was performed on a IBM PC/XT personal computer, and the computer language was BASIC. There are four possible configurations from which a user may choose. When running a program, the user is asked to enter the system parameters according to a specified control system configurations are; 1. A control system with a nonlinear element followed by a plant in a feedback configurations(NLSVF1). 2. A control system with a nonlinear device situated between two plants in a feedback configurations(NLSVF2). 3. A control system with a nonlinear element followed by a plant, followed by a the dealy in feedback configurations(TLAG). 4. A motor and load with a backlash nonlinearity between dynamic portions of the motor/load configurations (BACKLASH). The matrix from state equations are integrated using combination the trapezoidal method and fixed point iteration. Several cases which have nonlinearity were implemented on the computer and the results were discussed.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.206-209
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• 1988

For feedback control of a linear dynamic system the optimum linear slace regulator (OLSR) can be implemented only if all state are available for feedback. This work demonstrates that when only the output state is available for feedback, a nonlinear controllers can be improved performance over that obtained by a proportional controller. This paper found the optimal control law by well-known dynamic programming and principles of optimality. Thus, performance of both proportional and nonlinear controllers is compared with performance of optimum linear state regulator.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.284-284
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• 2000

In adaptive fuzzy control systems. fuzzy systems are used to approximate the unknown plant nonlinearities. Until now. most of the papers in the field of controller design for nonlinear system using fuzzy systems considers the affine system with fixed grid-rule structure based on system state availability. This paper considers observer-based nonlinear controller and dynamic fuzzy rule structure. Adaptive laws for fuzzy parameters for state observer and fuzzy rule structure are established so that the whole system is stable in the sense of Lyapunov.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1871-1876
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• 2005

In this paper, We present a new nonlinear adaptive control law using a disturbance accommodating control (DAC) observer for a Japanese automatic landing flight experiment vehicle called ALFLEX. A future spaceplane must have ability to deal with greater fluctuations in the stability and control derivatives of flight dynamics, because its flight region is much wider than that of conventional aircraft. In our previous studies, digital adaptive flight control systems have been developed based on a linear-parameter-varying (LPV) model depending on dynamic pressure, and obtained good simulation results. However, under previous control laws, it is difficult to accommodate uncertainties represented by disturbance and nonlinearity, and to design a stable flight control system. Therefore, in this study, we attempted to design a nonlinear adaptive control law using the DAC Observer and inverse dynamic methods. A good tracking property of the obtained system was confirmed in numerical simulation.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.1
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• pp.39-42
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• 2002

For the purpose of modeling EEG signal which has nonstationary and nonlinear dynamic characteristics, this paper propose a state feedback real time recurrent neural network model. The state feedback real time recurrent neural network is structured to have memory structure in the state of hidden layers so that it has arbitrary dynamics and ability to deal with time-varying input through its own temporal operation. For the model test, Mackey-Glass time series is used as a nonlinear dynamic system and the model is applied to the prediction of three types of EEG, alpha wave, beta wave and epileptic EEG. Experimental results show that the performance of the proposed model is better than that of other neural network models which are compared in this paper in some view points of the converging speed in learning stage and normalized mean square error for the test data set.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.281-286
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• 2003

A Suspension system of a car is composed of dampers and springs. The dampers and springs usually have nonlinear characteristics. However, the nonlinear characteristics of the springs and dampers through analytical model cannot agree with the experimental results. Therefore, the nonlinearity of the suing and damper should be known from the experimental results. In this study, the methods of system identification for nonlinear dynamic system in time domain are discussed and the nonlinear parameter estimation lot experimental data of an EF-SONATA car was done. The results show that a cubic and a coupled term should be considered to model the suspension system.

• Transactions of the Korean Society of Mechanical Engineers
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• v.14 no.6
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• pp.1426-1435
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• 1990

Dynamic behaviour of point tracking system mounted on flying vehicle shaking in a random manner is investigated and the system dynamic is represented by nonlinear stochastic equations. 2-D.O.F. nonlinear stochastic equations are successfully transformed to linear stochastic equations via equivalent linearization procedure in stochastic sense. Newly developed hybrid technique is used to obtain response statistics of the system under non-white random excitation, which is proved to be remarkably accurate one by performing stochastic simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.9
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• pp.929-938
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• 2009

This paper presents a novel control methodology for communication network based nonlinear systems with time delay nature. We construct a nominal nonlinear control law for representing a linear model and a reset control system which is aimed for corrective control strategy to compensate system error due to uncertain time delay through wireless communication network. Next, online neural control approach is proposed for overcoming nonstationary statistical nature in the network topology. Additionally, DBN (Dynamic Bayesian Network) technique is accomplished for modeling of its dynamics in terms of casuality, which is then utilized for estimating prediction of system output. We evaluate superiority and reliability of the proposed control approach through numerical simulation example in which a nonlinear inverted pendulum model is employed as a networked control system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.12
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• pp.562-570
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• 2002

The Purpose of this paper is to design and manufacture an educational system in order to demonstrate the causes and effects of electromagnetic induction.'rho educational system described in this study is a "jumping ring apparatus". This system demonstrates the principle of electromagnetic induction, a force from AC sources, Lenz's law of repulsion and transformer. The educational system is composed of a jumping ring apparatus, a sensor array, encoder, A/D converter, D/A converter and nonlinear controller. The educational system is controlled by 586 PC using Turbo C program. The sensor array is composed of 20 optical sensors. The nonlinear controller consists of nonlinear control algorithm and control board included SCR, FET and phase controller. The A/D converter is used to show the height of ring position to analog for an education purpose. The control signal calculated from the nonlinear control of algorithm send control board through 8 bit D/A convertor. Experiment results are given to verify that Proposed nonlinear controller is useful in on line control of the educational system.al system.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.413-419
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• 2009

A decentralized adaptive control method is proposed for large-scale systems with unknown time-delayed nonlinear interconnections unmatched in control inputs. It is assumed that the time-delayed interaction terms are bounded by unknown nonlinear bounding functions. The nonlinear bounding functions and uncertain nonlinear functions of large-scale systems are compensated by the function approximation technique using neural networks. The dynamic surface control method is extended to design the proposed memoryless local controller for each subsystem of uncertain nonlinear large-scale time delay systems. Therefore, although the interconnected systems consist of a large number of subsystems, the proposed controller can be designed simply. We prove that all the signals in the total closed-loop system are semiglobally uniformly bounded and the control errors converge to an adjustable neighborhood of the origin. Finally, an example is given to demonstrate the effectiveness and applicability of the proposed scheme.