• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.187-190
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• 2003

In this paper, we address the robust adaptive backstepping controller using fuzzy neural network (FHIN) for a class of uncertain output feedback nonlinear systems with disturbance. A new algorithm is proposed for estimation of unknown bounds and adaptive control of the uncertain nonlinear systems. The state estimation is solved using K-fillers. All unknown nonlinear functions are approximated by FNN. The FNN weight adaptation rule is derived from Lyapunov stability analysis and guarantees that the adapted weight error and tracking error are bounded. The compensated controller is designed to compensate the FNN approximation error and external disturbance. Finally, simulation results show that the proposed controller can achieve favorable tracking performance and robustness with regard to unknown function and external disturbance.

• Journal of Electrical Engineering and Technology
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• v.6 no.4
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• pp.519-526
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• 2011

An investigation of the mechanism of period-doubling bifurcation in a voltage mode controlled buck-boost converter operating in discontinuous conduction mode is conducted from the viewpoint of nonlinear dynamical systems. The discrete iterative model describing the dynamics of the close-loop is derived. Period-doubling bifurcation occurs at certain values of the feedback factor. Results from numerical simulations and experiments are provided to verify the evolution of perioddoubling bifurcation, and the results are consistent with the theoretical analysis. These results show that the buck-boost converters exhibit a wide range of nonlinear behavior, and the system exhibits a typical period-doubling bifurcation route to chaos under particular operating conditions.

• Bulletin of the Korean Mathematical Society
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• v.54 no.6
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• pp.1893-1912
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• 2017

In this paper, we aim to construct a nonstandard finite difference (NSFD) scheme to solve numerically a mathematical model for cholera epidemic dynamics. We first show that if the basic reproduction number is less than unity, the disease-free equilibrium (DFE) is locally asymptotically stable. Moreover, we mainly establish the global stability analysis of the DFE and endemic equilibrium by using suitable Lyapunov functionals regardless of the time step size. Finally, numerical simulations with different time step sizes and initial conditions are carried out and comparisons are made with other well-known methods to illustrate the main theoretical results.

• Journal of the Korean Society of Safety
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• v.14 no.4
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• pp.3-12
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• 1999

This research concentrates on the influence of non-linearities associated with impact for the nonlinear rocking behavior of rigid block subjected to one dimensional sinusoidal excitation of horizontal direction. The transition of two governing rocking equations, the abrupt reduction in the kinetic energy associated with impact, and sliding motion of block. In this study, two type of rocking vibration system are considered. One is the undamped rocking vibration system, disregarding energy dissipation at impact and the other is the damped rocking system, including energy dissipation and sliding motion. The response analysis using non-dimensional rocking equation is carried out for the change of excitation parameters and friction coefficient. The chaos responses were discovered in the wide response region, particularly, for the case of high excitation amplitude and their chaos characteristics were examined by the time history, Poincare map, power spectra and Lyapunov Exponent of rocking responses. The complex behavior of chaos response, in the phase space, were illustrated by Poincare map. The bifurcation diagram and Poincare map were shown to be effective in order to understand chaos of rocking system.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1640_1641
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• 2009

Dynamic friction and force ripple are the most predominant factors that affect the positioning accuracy of permanent magnet synchronous motor(PMSM) servo drives system, and it is desirable to compensate them in finite time with a continuous control law. In this paper, based on LuGre dynamic friction model, a robust adaptive skidding mode controller is proposed to compensate the nonlinear effect of friction and force ripple. The controller scheme consists of a PD component and a robust adaptive sliding mode controller for estimating the unknown system parameter. Using Lyapunov stability theorem, asymptotic stability analysis and position tracking performance are guaranteed. Simulation results well verify the feasibility and the effectiveness of the proposed scheme for high0precision motion trajectory tracking.

• 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.

• Journal of KSNVE
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• v.6 no.5
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• pp.595-605
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• 1996

If the control force designed on the basis of the mathematical model with parameter errors is applied to control the actual system, the closed-loop performance of the actual system will be degraded depending on the degree of the errors, In this study, the effect of parameter errors on the robustness of several natural controls has been analyzed and compared. Every asymptoic stability condition for the natural controls has been derived using Lyapunov approach, and the characteristics of the stability conditions has also been compared. The extent of deviation of the closed-loop performance from the designed one for the natural controls is derived using operator techniques, and evaluated by numerical method. It has been found that the optimal control, acceleration feedback control, and acceleration-position feedback control among the considered natural controls would be robust one with respect to the parameter errors.

• 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.