• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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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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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.411-415
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• 1994

An adaptive learning control scheme by use of multilayer neural networks for compensating for uncertainties in nonlinear dynamic system is examined. Multilayer neural networks are introduced to map the uncertainties in nonlinear dynamics and perform nonlinear state feedback. Parameters of neural networks are adjusted by conventional back-propagation algorithms modified with the projection operation. Effectiveness of the proposed scheme for tracking control are demonstrated through computer simulations.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.994-998
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• 2005

A new control method, called a simple model matching, has been recently developed by the author. This is very simple and be applied for linear and nonlinear discrete time systems with/without time lag. Based on this formulation, identification is examined in this paper using an interconnected neural network with the EBP-EWLS learning algorithm. With this result, a control method is also presented for a nonlinear discrete time system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.1993-1995
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• 2001

The subject of nonlinear control is an important area of automatic control. The behavior of nonlinear systems is much more complex. If the operating range of a control system is small, and if the involved nonlinearities are smooth, then the control system may be resonably approximated by a set of linear differential equations. This paper presents the deadbeat response attribute of some nonlinear systems, e.g., magnetic levitation, pendulum, van der pol oscillator etc.. The studied results through the computer simulation are shown a promising attribute of deadbeat response that the outputs of the systems are reached relatively fast the steady state.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.327-330
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• 1989

Two recent nonlinear control methods of nonlinear geometric method and nonlinear optimal control method are applied to a pH control system with severe nonlinear characteristics. Simulations and experiments show that their performances are very good and robust for various realistic situations such as model parameter mismatches.

• International Journal of Control, Automation, and Systems
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• 제5권5호
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• pp.501-507
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• 2007

This paper presents a new algorithm for the closed-loop $H_{\infty}$ control of a class of singularly perturbed nonlinear systems with an exogenous disturbance, using the successive Galerkin approximation (SGA). The singularly perturbed nonlinear system is decomposed into two subsystems of a slow-time scale and a fast-time scale in the spirit of the general theory of singular perturbation. Two $H_{\infty}$ control laws are obtained to each subsystem by using the SGA method. The composite control law that consists of two $H_{\infty}$ control laws of each subsystem is designed. One of the purposes of this paper is to design the closed-loop $H_{\infty}$ composite control law for the singularly perturbed nonlinear systems via the SGA method. The other is to reduce the computational complexity when the SGA method is applied to the high order systems.

• Journal of Electrical Engineering and Technology
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• 제7권5호
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• pp.672-680
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• 2012

This paper proposes the control strategy of the shunt Flexible AC Transmission System (FACTS) devices to improve transient stability in multimachine power system. The multimachine power system has high nonlinear response after severe disturbance. The concept of Lyapunov energy function is applied to derive nonlinear control strategy and it was found that the time derivative of line voltage is not only can apply to control the shunt FACTS devices in multimachine system but also is locally measurable signal. The fuzzy logic control is also applied to overcome the uncertainty of various disturbances in multimachine power system. This paper presents the method of investigating the effect of the shunt FACTS devices on transient stability improvement. The proposed control strategy and the method of simulation are tested on the new England power system. It was found that the shunt FACTS devices based on the proposed nonlinear control strategy can improve transient stability of multimachine power system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.185-190
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• 1991

In this paper, we present a method of analising a system with nonlinear parameter by pole sensitivity defined by the rate of pole movement with respect of non-linear parameter variation. Pole sensitivity give us not only the rate of pole movement but also the directional information. We present a method of design of a state feedback for a system with nonlinear system parameter by considering the pole sensitivity and show that the suggested method guarantee the stability robustness for a system with nonlinear parameter, parameter perturbation and urimodelled dynamics.

• 한국항해학회지
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• 제23권3호
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• pp.35-44
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• 1999

This research aims to seek active control of ball-beam position stability by resorting to neural networks whose layers are given bias weights. The controller consists of an LQR (linear quadratic regulator) controller and a neural networks controller in parallel. The latter is used to improve the responses of the established LQR control system, especially when controlling the system with nonlinear factors or modelling errors. For the learning of this control system, the feedback-error learning algorithm is utilized here. While the neural networks controller learns repetitive trajectories on line, feedback errors are back-propagated through neural networks. Convergence is made when the neural networks controller reversely learns and controls the plant. The goals of teaming are to expand the working range of the adaptive control system and to bridge errors owing to nonlinearity by adjusting parameters against the external disturbances and change of the nonlinear plant. The motion equation of the ball-beam system is derived from Newton's law. As the system is strongly nonlinear, lots of researchers have depended on classical systems to control it. Its applications of position control are seen in planes, ships, automobiles and so on. However, the research based on artificial control is quite recent. The current paper compares and analyzes simulation results by way of the LQR controller and the neural network controller in order to prove the efficiency of the neural networks control algorithm against any nonlinear system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1987년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 16-17 Oct. 1987
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• pp.346-351
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• 1987

In this study, digital simulation with nonlinear modeling is carried out to analyse the performance of a hydraulic servomotor system developed for the position control of a large inertia. Nonlinear element, such as nonlinear pressure flow relationships of servovalve, valve spool limits, nonlinear friction, and backlash and resilience of gear system are included in the simulation along with the dynamic characteristics of variable delivery pump compensation mechanism. Simulation results are compared with experimental results for both step and sinusoidal inputs. Independent of input magnitude, both results are in good agreement with minor differences in detail.