• 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.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.2
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• pp.127-136
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• 1989

This paper describes the parameter convergence properties of an adaptive system to identify a single-input single-output plant model. It is demonstrated that, by using power spectrum analysis, the persistency of excitation (PE) condition in order to guarantee the exponential stability of the adaptive control system can be transformed into the positive definite behavior for the auto-correlation function matrix of adaptive signal. The existence of parameter nominal values can be analyzed by this condition and the convergence rates of parameter are determined by examining the auto-correlation function. We may use the sufficient richness (SR) of input spectrum instead of the PE condition to analyze the parameter boundedness. It can be shown that the eigen values of the auto-correlation function are always related with adaptive gain, input amplitude and positions or numbers of input spectra. In each case, the variation of parameter convergence rate can be also verified.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.255-266
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• 2007

A Bayesian estimation of the four-parameter gamma distribution is considered under the noninformative prior. The Bayesian estimators are obtained by the Gibbs sampling. The generation of the shape/power parameter and the power parameter in the Gibbs sampler is implemented using the adaptive rejection sampling algorithm of Gilks and Wild (1992). Also, the location parameter is generated using the adaptive rejection Metropolis sampling algorithm of Gilks, Best and Tan (1995). Finally, the simulation result is presented.

• Journal of Drive and Control
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• v.17 no.1
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• pp.13-20
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• 2020

Adaptive neural networks based lateral controller is presented to guarantee path following performance for vehicle lane keeping in the presence of parameter time-varying characteristics of the vehicle lateral dynamics due to the road surface condition, load distribution, tire pressure and so on. The proposed adaptive controller could compensate vehicle lateral dynamics deviated from nominal dynamics resulting from parameter variations by incorporating it with neural networks that have the ability to approximate any given nonlinear function by adjusting weighting matrices. The controller is derived by using Lyapunov-based approach, which provides adaptive update rules for weighting matrices of neural networks. To show the superiority of the presented adaptive neural networks controller, the simulation results are given while comparing with backstepping controller chosen as the baseline controller. According to the simulation results, it is shown that the proposed controller can effectively keep the vehicle tracking the pre-given trajectory in high velocity and curvature with much accuracy under parameter variations.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.4
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• pp.259-266
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• 1987

In order to drive motor control system precisely, the motor is to be controlled by mmfs and current with sinusoidal waveforms. In this paper the Delta Modulation (DM) Technique is used for generating PWM pulse with sinusoidal waveform. However the motor currents yet contain odd harmonics due to leakage inductances, speed and exitation. To reduce harmonics, the parameter adaptive control method is introduced. That is, Req.C parameter of Delta Modulator is controlled adaptively by parameter adaptor. The adaptive signal is achieved by the difference between motor current and reference waveform, and this signal is converted to the voltage commend signal by adaptive mechanism. The test reslts show that this system is operated smoothly over a wide range of motor speed and motor current is controlled to be sinusoidal waveform adaptively.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.706-709
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• 2005

We study the adaptive stabilization of the Van der Pol equation. A parameter update law is designed by the immersion and invariance method, and is used in conjunction with both the feedback linearization and backstepping control laws. Simulation results show that the responses obtained in the adaptive case are very similar to the known parameter case, and the parameter estimator converges to the true value.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.10
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• pp.740-747
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• 1988

This paper describes the parameter convergence property for an adaptive identifier and deals with the stability of the adaptive system in terms of the general error model. The Persistent Excitation (PE) condition to guarantee parameter convergence is derived using the Power Spectrum Analysis. In the adaptive identifier designed under the assumptions that the plant has not unmodelled dynamics, it can be shown that the equilibrium points of adjustable parameters are independent on the position or the number of input spectrums, if the adaptive signal is PE. When the plant contains unmodelled dynamics and the same controller is used, the PE condition can still hold but the parameter tuned values are changed with the spectrum.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.5
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• pp.977-983
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• 2011

This paper proposes a new adaptive speed controller to achieve a robust speed control of a permanent magnet synchronous motor(PMSM). The proposed adaptive regulator does not require any information on the motor parameter and load torque values, so it is very insensitive to model parameter and load torque variations. Also, the stability of the proposed adaptive control system is proven. To validate the robustness of the proposed adaptive speed controller, both simulation and experimental results are provided under motor parameter and load torque variations. It is clearly demonstrated that the proposed adaptive regulator can accurately control the speed of permanent magnet synchronous motors.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.337-340
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• 1997

In this paper we propose direct and indirect adaptive controllers for a nonlinear multivariable steam generating unit(200MW). In the direct adaptive scheme the estimation of the controller parameter are achieved from tracking error, while in the indirect approach the unknown parameter of the boiler system is estimated by the Hopfield network-based identifier. The performance of two proposed adaptive controllers is shown through simulations.

• The Transactions of the Korean Institute of Power Electronics
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• v.24 no.1
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• pp.33-39
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• 2019

We propose a mechanical parameter estimation algorithm for surface-mounted permanent magnet synchronous motors (SPMSMs) using a sliding-mode observer (SMO) and an adaptive filter. The SMO estimates system disturbances in real time, which contain the information on mechanical parameters. A desirable feature that distinguishes the proposed estimation algorithm from other existing mechanical parameter estimators is that the adaptive filter estimates electromagnetic torque to improve the estimation performance. Moreover, the SMO acts as a low-pass filter to suppress the chattering effect, which enables the smooth output signals of the SMO. We verify the mechanical parameter estimation performance for SPMSM by conducting extensive experiments for the proposed algorithm.