• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.473-479
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• 2016

This paper describes an accurate and stable current control method of switched reluctance motors(SRMs), which have recently attracted considerable wide attention owing to their favorable features, such as high performance, high durability, structural simplicity, low cost, etc. In most cases, the PI controllers(PICC) have been used mostly for the current control of electric motors because their algorithm and selection of controller gain are relatively simpler compared to other controllers. On the other hand, the PI controller requires an adjustment of the controller gains for each operating point when nonlinear system parameters change rapidly. This paper presents a stable current control method of an SRM using self-tuning fuzzy current controller(STFCC) under nonlinear parameter variation. The performance of the considered method is validated via a dynamic simulation of the current controlled SRM drive using Matlab/Simulink program.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.1-10
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• 2007

This paper presents a direct nonlinear multivariable self-tuning PID controller using neural network which adapts to the changing parameters of the nonlinear multivariable system with noises and time delays. The nonlinear multivariable system is divided linear part and nonlinear part. The linear controller are used the self-tuning PID controller that can combine the simple structure of a PID controllers with the characteristics of a self-tuning controller, which can adapt to changes in the environment. The linear controller parameters are obtained by the recursive least square. And the nonlinear controller parameters are achieved the through the Back-propagation neural network. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation results are presented to adapt the nonlinear multivariable system with noises and time delays and with changed system parameter after a constant time. The proposed PID type nonlinear multivariable self-tuning method using neural network is effective compared with the conventional direct multivariable adaptive controller using neural network.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.22-33
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• 1999

This paper presents the method for the automatic tuning of a design weighting polynomial parameters of a generalized minimum-variance stochastic ultivariable self-tuning controller which adapts to changes in the higher order nonminimum phase system parameters with time delays and noises. The self-tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing the design weighting polynomial parameters of the controller. The proposed multivariable self-tuning method is simple and effective compared with pole restriction method. The computer simulation results are presented to adapt the higher order multivariable system with nonminimum phase and with changeable system parameters.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.2
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• pp.160-168
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• 1997

A $H_{\infty}$self-tuning control scheme for the tip position of a flexible link robot handling unknown loads is presented here. The scheme essentially comprises a recursive least-squares identification algorithm and $H_{\infty}$self-tunning controller. The $H_{\infty}$control low is designed to be robust to uncertain parameters and the self-tunning action provides adaption to unknown parameters. Through numerical study, the performance comparison of the $H_{\infty}$self-tuning controller with a constant gain $H_{\infty}$controller as well as a LQG self-tuning controller clearly shows its superior ability in handling load changes in quiescent states.nt states.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.4
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• pp.21-28
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• 2004

This paper presents a direct multivariable self-tuning controller using neural network which adapts to the changing parameters of the higher order multivariable nonlinear system with nonminimum phase behavior, mutual interactions and time delays. The nonlinearities are assumed to be globally bounded, and a multivariable nonlinear system is divided linear part and nonlinear part. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm the computer simulation is done to adapt the multivariable nonlinear nonminimm phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct multivariable adaptive controller using neural network.

• The Transactions of the Korean Institute of Power Electronics
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• v.9 no.3
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• pp.240-245
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• 2004

This paper presents a Switched Reluctance Motor(SRM) drive using the self-tuning control method to achieve the maximum torque. SRM has the difficulty to research it by an analytic method and to control the speed End torque because of the high nonlinearity. So, in this paper, the self-tuning control method is applied to relevantly controlling turn-on/off angle to operate at the maximum torque. Also, the feedback signals to control the turn-on/off angle are the encoder pulse and the increment of phase current. At first, n adequate turn-off angle is searched by itself and then a turn-on angle is done. As the relationship between turn-on and him-off angle is mutual dependent, the turn-on/off angle is controlled by a real time self-tuning control method in order to maintain the maximum torque. The proposed self-tuning Algorithm is verified by experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.355-367
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• 2007

This paper proposes a design method of the PI(Proportional-Integral)+D(Derivative) controller using self-tuning of the design variables and controller gains. The used fuzzy PI+D controller is the approximated conventional continuos time linear PI+D controller and the used fuzzification method is the fuzzy single tone and the adapted defuzzification method is the simplified tenter of gravity. Fuzzy estimation result would be calculated in the other function elements from the classified fuzzy variables and the result determined by the design variables decides the controller gains. As a result, the proposed method shows the capability of the high speed tuning and can be applied to the case of input variables with many fuzzy partitions and also can bring out the advantage to reduce the reconstruction(digital sampling reconstruction) error. Most simulation results show that this controller makes much bettor efficiency and improvement by using design variables and controller gains.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.348-354
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• 1999

In recent years, a PID controller has been used as a major control method in real control processes. This controller requires a determination of PID control gains. But it is difficult to select the best gains theoretically. Thus there have been many approaches to determine them empirically Most of them are based on experience and knowledge. In this paper, we proposed a tuning method of the PID Parameters by using neural network. To show effectiveness of the proposed method, the simulation of DC motor and one link manipulator position control is carried out.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.66-68
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• 2004

PID제어기를 플랜트 파라미터를 이용하여 구성하는 IMC-PID제어에 대해 연구한다. 특히 변하는 플랜트에 대해서는 자기동조(ST, Self-Tuning)를 사용하여 시스템을 식별하여 활용한다. 특히 실시간으로 개루프의 위상여유 및 이득여유를 모니터링하여 정해진 구역을 벗어나게 되면 식별된 시스템파라미터를 이용하여 IMC-PID제어기를 구성한다. 또 시간영역 지표로 과도한 오차가 발생하는 경우에도 제어기를 갱신함으로서 전체적으로 보면 견실 PID제어기 형태를 갖게 한다.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.826-827
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• 2015

본 논문은 비선형적인 파라미터 특성을 갖는 Switched Reluctance Motor(SRM)의 강인제어를 위한 보다 정확하고 안정적인 전류제어 방법에 대해 설명한다. 기존의 전류제어 방법에는 이득설정이 간편한 PI 제어기를 이용한 방법이 주로 사용되어왔다. 본 논문에서는 자기동조 퍼지 제어기를 이용한 방법과 Dead-Beat 제어기를 이용한 방법을 통하여 파라미터의 변화에도 안정적인 전류제어가 가능한 것을 보였고 PI 전류 제어기와 자기동조 퍼지 전류 제어기, 그리고 Dead-Beat 전류 제어기를 각각 적용한 전류제어 SRM 드라이브의 Matlab/Simulink 시뮬레이션 결과를 통하여 제어성능을 비교분석하였다.