• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.433-436
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• 1998

The objective of this paper is to design a adaptive fuzzy controller for autonomous navigation of mobile robot. The adaptive fuzzy controller has an advantage in data processing time and convergence speed. The basic idea of control is to induct membership function and fuzzy inference rules and to scale inducted membership function to suitable robot state. The adaptive fuzzy control method is applied to mobile robot and the simulation results show the effectiveness of our controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.185-188
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• 1998

In this paper, we propose a feedback adaptive controller which need not adjustment of the scale factor. Numerical examples are included to illustrate the procedure of a adaptive control and to show the performance of the control system. We can observe that the output of control system, converges toward the reference of response.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.6
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• pp.420-424
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• 2003

A new adaptive filter is proposed for an aided strapdown inertial navigation system(SDINS). The proposed filter can be used to effectively estimate the time-varying variance of the measurement noise. Then, the in-flight alignment for the radar aided SDINS is designed using the additive quatermion error model. Simulation results show that the proposed adaptive filter effectively improves the performance of the radar aided SDINS.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.53 no.4
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• pp.201-204
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• 2004

In this paper, a direct adaptive tracking controller based Lyapunov method is designed for a wheeled mobile robots. A wheeled mobile robots have three degrees of freedom and two control variables. Therefore, it is difficult to control a mobile robot using the general linear control. We introduce two kinds of Lyapunov function for the design of the controller and verify the controller. A mobile robots using the designed adaptive direct tracking controller is well-behaved and is easily implemented.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.453-456
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• 1996

In this paper, the direct adaptive control using neural networks is presented for the control of chaotic nonlinear systems. The direct adaptive control method has an advantage that the additional system identification procedure is not necessary. In order to evaluate the performance of our controller design method, two direct adaptive control methods are applied to a Duffing's equation and a Lorenz equation which are continuous-time chaotic systems. Our simulation results show the effectiveness of the controllers.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.56.3-56
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• 2001

This study describes the jump error smoothing scheme with fuzzy logic based on the scalar adaptive filter. The scalar adaptive filter is an useful algorithm for smoothing abrupt jump errors. However, the performances of scalar adaptive algorithm depend on the variance of real signal. So to design an effective algorithm, many informations of real and jump signal are required. In this paper, the fuzzy rules are designed by the analysis of scalar adaptive filter, and then the improved and simplified scheme is developed for smoothing the jump error. Simulations to INS/GPS integrated system show that the proposed method is effective.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.39.2-39
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• 2001

This paper describes the design of an output-feedback controller based on an adaptive fuzzy observer for uncertain single-input single-output nonlinear dynamical systems. Especially, we have focused on the realization of minimal dynamic order of the adaptive fuzzy observer. For the purpose, we propose a new method in which no strictly positive real(SPR) condition is needed and combine dynamic rule activation scheme with on-line estimation of fuzzy parameters. By using proposed scheme, we can reduce computation time, storage space, and dynamic order of the adaptive fuzzy observer ...

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.45-49
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• 1988

This paper presents a centralized adaptive control scheme based on perturbation equations in the vicinity of a desired trajectory,which are used to design a feedback control about the desired trajectory. This adaptive control scheme reduces the manipulator control problem from a nonlinear control to controlling a linear control system about a desried trajectory. Computer simulation studies of a two-joint manipulator are performed on a IBM-PC to illustrate the performance of this adaptive control scheme.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.535-535
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• 2000

This paper presents an adaptive receding horizon H$_{\infty}$ controller for the linear parameter varying systems in the deterministic environment, which combines a parameter range estimator and a robust receding horizon H$_{\infty}$ controller using the parameter bounds. Using parameter set inclusion and terminal inequality condition, the closed-loop system stability is guaranteed. It is shown that the stabilizing adaptive receding horizon H$_{\infty}$ controller guarantees the H$_{\infty}$ norm bound.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.145-145
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• 2000

In this paper, we propose a hybrid d-step predictor which is composed of an adaptive predictor and a Kalman predictor. We prove the performance limit of the proposed predictor. Simulation is conducted to examine the performance of the proposed predictor. Simulation results show that the proposed combined predictor is superior to the adaptive predictor and the Kalman predictor. Proposed predictor is used for prediction of gun tip vibration of k1 tank. The result is compared with that of conventional adaptive predictor.