• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.122-127
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• 2001

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-neuro control scheme is proved to be a efficient control technique for real-time control of robot system using DSPs.

• Proceedings of the KIEE Conference
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• 2007.11c
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• pp.160-164
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• 2007

MRAS observer, which is based on adaptive control theory, estimates speed and position by using optimal observer gains on the basis of Lyapunov stability theory. However, in case of MRAS theory, position estimation error is in existence because of non-linearity for inductance variation and limit cycles for position estimation. The adaptive sliding observer based on the variable structure control theory estimates the speed and position for zero of estimation error by using the sliding surface equal to the error between speed and position estimation. The binary observer estimates the rotor speed and rotor flux with alleviation of the high-frequency chattering, and retains the benefits achieved in the conventional sliding observer, such as robustness to parameter and disturbance variations. The speed and position sensorless control of SRM under the load and inductance variation is verified by the experimental results.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2681-2683
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• 2005

In this paper, we present the structure of fuzzy neural network(FNN) based on wavelet function, and apply this network structure to the identification of nonlinear systems. For adjusting the shape of membership function and the connection weights, the parameter learning method based on the gradient descent scheme is adopted. And an approach that uses adaptive learning rates is driven via a Lyapunov stability analysis to guarantee the fast convergence. Finally, to verify the efficiency of our network structure. we compare the Identification performance of proposed wavelet based fuzzy neural network(WFNN) with those of the FNN, the wavelet fuzzy model(WFM) and the wavelet neural network(WNN) through the computer simulation.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1848-1849
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• 2006

The new robust controller design method is proposed for the flight control systems with model uncertainties. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides us with the ability to overcome the "explosion of complexity" problem of the backstepping controller. The SRWNNs are used to observe the arbitrary model uncertainties of flight systems and all their weights are trained on-line. From the Lyapunov stability analysis, their adaptation laws are induced and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a high performance aircraft (F-16) are utilized to validate the good tracking performance and robustness of the proposed control system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.472-477
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• 1996

This paper presents a new approachtothe design of adaptive control system using DSPs(TMS320C30) for robotic manipulators to achieve trajectory tracking by the joint angles. Digital signal processors are used in implementing real time adaptive control algorithms to provide an enhanced motion control for robotic manipulators. In the proposed control scheme, adaptation laws are derived from the improved Lyapunov second stability analysis method based on the adaptive model reference control theory. The adaptive controller consists of an adaaptive feedforward controller, feedback controller, and PID type time-varying auxillary control elements. The prpposed adaptive control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Moreover, this scheme does not require an accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the adaptive controller is illustrated by simulation and experimental results for a SCARA robot.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.12
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• pp.518-525
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• 2006

This paper presents the adaptive robust control method for the flight control systems with model uncertainties. The proposed control system can be composed simply by a combination of the adaptive dynamic surface control (DSC) technique and the self recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides us with the ability to overcome the 'explosion of complexity' problem of the backstepping controller. The SRWNNs are used to observe the arbitrary model uncertainties of flight systems, and all their weights are trained on-line. From the Lyapunov stability analysis, their adaptation laws are induced and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a high performance aircraft (F-16) are utilized to validate the good tracking performance and robustness of the proposed control system.

• Proceedings of the KIEE Conference
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• 2001.05a
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• pp.172-175
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• 2001

This paper presents a new energy function reflecting the damping effect in multi-machine power systems. The Lyapunov direct method provides precise and rigorous theoretical backgrounds for stability analysis of nonlinear systems. Incorporating damping effects into accurate estimates of the domain of attraction, which is a minor but crucial point, has been attempted with long history to yield partial success for single machine systems. In this paper, the damping-reflected energy function presented in the previous work has been generalized for application to multi-machine systems. The generalized energy function is tested for the WSCC 9-bus system to show the semi-negativeness of its time derivative.

• Proceedings of the KIEE Conference
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• summer
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• pp.242-244
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• 2004

본 논문에서는 전력계통에서 에너지 함수를 이용하여 탭의 영향이 고려된 전압안정도 해석방법을 제시하였다. 전력계통의 전압안정도 해석은 부하단 변압기 탭의 특성과 밀접하게 연관되어 있으며 역학적 등가모델(EMM)을 이용하여 탭의 영향을 고려한 새로운 에너지함수를 유도하였다. 이 에너지 함수는 Lyapunov 함수의 조건을 만족시키며, 유도된 에너지함수로부터 안정평형점과 불안정평형점을 구하여 두 점에서의 에너지의 차이로서 전압붕괴를 예측할 수 있음을 1기 무한대 모선에 대하여 검증해 보았다.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.45.3-45
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• 2002

$\textbullet$ In this paper, a new method of estimating the rotor speed and flux is proposed. $\textbullet$ The stator currents and voltages are measurable and all the system parameters are known. $\textbullet$ There are a number of common terms in the error dynamics. $\textbullet$ This is utilized to find a simpler error model involving some auxiliary variables. $\textbullet$ Using this error model, the state estimation problem is converted into a parameter estimation prob. $\textbullet$ Some stability properties are given on the basis of Lyapunov analysis. $\textbullet$ The effectiveness of the proposed scheme is demonstrated through simulations and experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.3
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• pp.171-176
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• 2016

In this paper, we consider a regulation problem of nonlinear systems under two triangular conditions where there possibly exist unbounded parameters in the systems. We propose a state feedback controller with dynamic gains in order to deal with unbounded parameters based on the condition of the time-varying rate of the growing parameter. The analysis of our control scheme is carried out by Lyapunov stability method. Our control method is verified by simulation results.