• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.139-144
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• 1993

In this paper, a robust reduced order adaptive controller is proposed based on Internal Model Control(IMC) structure for stochastic linear stable systems. The concept of gain margin is utilized to make the adaptive IMC controller robust. We prove the stability of the proposed adaptive IMC system for stable plants under the assumption that upper bounds for system orders are known. Simulation results show that the proposed method has good performance and stability robustness.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1688-1691
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• 1997

In the time-invarient system, the adaptive controller was designed for the non-tracking error in the 1980's. In this study, the Model Reference Adaptive Control using on-line processing method is used to identify the coefficients of the model, and the Robust Controller (H.inf.) is designed to stabilize the rigid body and the flexible body of satellite, which can be perturbed due to disturbance, etc. The result obtained by H.inf. controller is compared with that of the PI(Proportional and Intergation) controller which is commonly used for stabilizing satellite.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 추계종합학술대회 논문집
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• pp.437-440
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• 1998

In this paper, the design method of fuzzy neural network(FNN) controller using indirect adaptive control technique is presented for controlling chaotic nonlinear systems. Firstly, the fuzzy model identified with a FNN in off-line process. Secondly, the trained fuzzy model tunes adaptively the control rules of the FNN controller in on-line process. In order to evaluate the proposed control method, Indirect adaptive control method is applied to the representative continuous-time chaotic nonlinear systems, that is, the Duffing system and the Lorenz system. Simulations are done to verify the effectivencess of controller.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 추계학술대회
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• pp.737-742
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• 2004

In this paper, a family of decentralized adaptive controller is proposed to control robot manipulators which are governed by highly nonlinear dynamic equations. The controller is computationally efficient since it does not require mathematical model or parameter values of the manipulators. The stability of the manipulators with the controller is proved by Lyapunov theory. The results of numerical simulations show that the system is stable, and has excellent trajectory tracking performance.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.161.4-161
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• 2001

This paper presents adaptive control of robot manipulator using neuro-fuzzy controller Fuzzy logic is control incorrect system without correct mathematical modeling. And, neural network has learning ability, error interpolation ability of information distributed data processing, robustness for distortion and adaptive ability. To reduce the number of fuzzy rules of the FLS(fuzzy logic system), we consider the properties of robot dynamic. In fuzzy logic, speciality and optimization of rule-base creation using learning ability of neural network. This paper presents control of robot manipulator using neuro-fuzzy controller. In proposed controller, fuzzy input is trajectory following error and trajectory following error differential ...

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.125.3-125
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• 2001

Recently, aircraft is designed to have high maneuverable at high angle of attack. However, it is very hard to obtain the accurate dynamic model for the high performance, because aerodynamic characteristics are nonlinear and include a lot of uncertainties. Therefore, nonlinear controller without considering uncertainties may degrade the control system performance. On this paper, to overcome these defects, the neural networks based adaptive nonlinear controller is proposed making use of the backstepping technique. Neural networks are implemented to guarantee robustness to uncertainties caused by aerodynamic coefficients variation. The main feature of the proposed controller is that the adaptive controller is developed under the assumption ...

• 대한전자공학회논문지
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• 제25권3호
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• pp.262-268
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• 1988

In this paper conditions are derived, which ensure the existence of a quasi-sliding mode on the control switching hyperplane in discrete variable structure control systems and also remove the reaching phase problem observed in continuous-time variable structure systems. In addition, a discrete variable structure tracking controller which has adaptive properties is devised based on these results. This controller has useful properties, such as small sensitivity to the variation of plant parameters and to disturbances and its performing speed is fast compared to that of other adaptive controller.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권4호
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• pp.294-300
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• 2009

The neural network with radial basis function is introduced for position tracking control of AC servo drive with the existence of system uncertainties. An adaptive robust term is applied to overcome the external disturbances. The proposed controller is implemented on a high performance digital signal processing DSP TMS320C6713-300. The stability and the convergence of the system are proved by Lyapunov theory. The validity and robustness of the controller are verified through simulation and experimental results

• 대한전자공학회논문지
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• 제22권2호
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• pp.31-35
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• 1985

This paper presents novel designs of a digital minimum-time controller and a digital adaptive controller using reference no del for single input-single out put linear time-invariant plants with known parameters. The proposed digital minimum-time controller which has a deadbeat response is designed to make the transfer function of this controller equal to that of reference model, and the proposed digital adaptive controller is designed by applying the adaptation method to the proposed digital minimum-time controller. The designs of these two controllers are very simple and easy, and all types of input signal with any reference models are controllable. These dffectivenesses have been demon-strated by computer simulations carried out for a third-order plant.

• Journal of Advanced Marine Engineering and Technology
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• 제30권7호
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• pp.791-799
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• 2006

The aim of this paper is to design an adaptive speed control system of a marine diesel engine by fusion of hard computing based proportional integral derivative (PID) control and soft computing based fuzzy control methods. The model of a marine diesel engine is considered as a typical non oscillatory second order system. When its model and the actual marine diesel engine ate not matched, it is hard to control the speed of the marine diesel engine. Therefore, this paper proposes two methods in order to obtain the speed control characteristics of a marine diesel engine. One is an efficient method to determine the PID control parameters of the nominal model of a marine diesel engine. Second is a reference adaptive speed control method that uses a fuzzy controller and derivative operator for tracking the nominal model of the marine diesel engine. It was found that the proposed PID parameters adjustment method is better than the Ziegler & Nichols' method, and that a model reference adaptive control is superior to using only PID controller. The improved control method proposed here, could be applied to other systems when a model of a system does not match the actual system.