• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.9
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• pp.1676-1679
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• 2007

In control of the time delay systems, if the disturbance information can be estimated exactly, the ideal response for the disturbance can be achieved. Therefore, in this paper, a new adaptive disturbance observer for the time delay system control is proposed. The disturbance observer is designed and adaptive laws for adjusting the observer are proposed. The new Smith predictor controller to eliminate the effects of the disturbance can be achieved by adding an estimated disturbance to the Smith model. The improved performances of the proposed system and the adaptive disturbance observer are verified by computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.401-405
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• 1993

This paper presents a neuro adaptive control method for nonlinear dynamical systems based on artificial neural network systems. The proposed neuro adaptive controller consists of 3 layers artificial neural network system and parallel PD controller. At the early stage in learning or identification process of the system characteristics the PD controller works mainly in order to compensate for the inadequacy of the learning process and then gradually the neuro contrller begins to work instead of the PD controller after the learning process has proceeded. From the simulation studies the neuro adaptive controller is seen to be robust and works effectively for nonlinear dynamical systems from a practical applicational points of view.

• Journal of Drive and Control
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• v.14 no.4
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• pp.37-44
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• 2017

This paper introduces a control algorithm for trajectory control of an electro-hydrostatic rotary actuator. A key feature of this paper is that an adaptive PID based on sliding mode is used to control the nonlinearity and uncertainty factor of single input/output system. Accurate knowledge of rotary actuator angle can result in high-performance and efficiency of electro hydraulic system. First, the position control is formulated using the adaptive PID with sliding mode technique and uncertainties in the hydraulic system. Second, the controller can update the PID gains on-line based on error caused by external disturbance and uncertain factors in the system. Finally, three experimental cases were studied to evaluate the proposed control method.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.5
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• pp.326-331
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• 2016

This paper presents a novel robust adaptive AC8B exciter system against synchronous generators for ships. A PID (proportional integral derivative) control framework, which is a part of the AC8B exciter system, is simply composed of nominal and auxiliary control configurations. For selecting these proper parameter values, the former is conventionally chosen based on the experience and knowledge of experts, and the latter is optimally estimated via a neural networks optimization procedure. Additionally, we propose an online parameter learning-based auxiliary control to practically cope with deterioration of control performance owing to uncertainty in electric generator systems. Such a control mechanism ensures the robustness and adaptability of an AC8B exciter to enhance control performance in real-time implementation. We carried out simulation experiments to test the reliability of the proposed robust adaptive AC8B exciter system and prove its superiority through a comparative study in which a conventional PID control-based AC8B exciter system is similarly applied to our simulation experiments under the same simulation scenarios.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.160-163
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• 1988

In this paper, the adaptive controller for the turret gun is discussed which uses model reference adaptive technique based on the Lyapunov direct method. Turret gun can be decomposed into two time-invariant SISO control systems. One is for the elevation angle control and the other is for the azimuth angle control under the assumption of independence each other. Thus we only consider here about the control loop for the elevation angle.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.330-335
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• 1994

In this paper, adaprive control and sliding mode control are combined to implement the proposed adaptive sliding mode control(ASMC) algorithm which is new approach to the control of industrial robot manipulator with external disturbances and parameter uncertainties. Adaptive control algorithm is designed by using the principle of the model reference adaptive control method based upon the hyperstability theory. The contribution of this method is that the parameters of the sliding surface are replaced by time varying parameters whose are calculated by an adaptation algorithm, which forces the errors to follow the behavior of a reference error model. Simulation results show that the proposed method not only improves the performance of the system but also reduces the chattering problem of sliding mode control. Consequently, it is expected that the new adaptive sliding mode control algorithm will be suited for various practical applications.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.3
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• pp.651-659
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• 1999

This Paper Proposes new speed and electromagnetic torque control of an induction motor, which is robust against time varying parameters. The control is based on adaptive vector control with serial block adaptive algorithm. Motor parameters used to estimates slip frequency and electromagnetic torque. Parameters mismatch in the control system detrimentally affects slip frequency estimation and torque response. In order to compensate lot degradation of the responses, an adaptive identifier for the magnetizing inductance and the secondary time constant is introduced. adaptive vector control system consisted of two subsystems, a vector control system realized on synchronous frame and a parameter identification system on stationary frame. the effectiveness of the proposed method was verified by some digital simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.11
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• pp.928-934
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• 2002

A natural way to cope with fault tolerant control (FTC) problems is to modify the control parameters according to an online identification of the system parameters when a fault occurs. However. due to not only difficulties Inherent to the online multivariable identification in closed-loop systems, such as modeling errors, noise or the lack of excitation signals, but also long time requirement to identify the post-fault system and implemeutation of control problems during the identification process, we propose an alternative approach based on the observer-based fault detection and isolation (FDI) and model reference adaptive control (MRAC). The proposed robust fault diagnosis method is based on a bank of observers. We also propose a model reference adaptive control with changeable reference models according to the occurred faults. Simulation results of a flight control example show the validity and applicability of the proposed algorithms.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.4
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• pp.101-107
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• 1996

This test was performed on the hydraulic automatic gauge control(AGC) system of adaptive mass flow method. Fundamental purpose of this study are performance evaluation of this AGC system under the actual rolling condition. It was concluded that the response of AGC system depends on the dynamic characteristics of a reel motor or roll position. The test results are as follows : 1) The control method of reel motor current is better than than of the roll position as AGC system. 2) The more steel strip thickness of delivery side is thick, the larger the gauge deviation is large, and the more it is thin, the larger the gauge deviation rate is large. 3) Because the gauge deviation is large at acceleration and deceleration speed than steady speed, so AGC system is better to adopt over 50m/min. By applying this AGC system, not only the accurary in strip thickness were improved but also productivity was improved dramatically.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.215-218
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• 1997

A dynamic back-propagation neural network is addressed for adaptive neural control system to approximate non-linear control system rather than static networks. It has the capability to represent the approximation of nonlinear system without mathematical analysis and to carry out the on-line learning algorithm for real time application. The simulated results show fast tracking capability and adaptive response by using dynamic back-propagation neurons.