• Proceedings of the KIEE Conference
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• 2004.04a
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• pp.147-149
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• 2004

This paper presents a new velocity estimation strategy of a non-salient permanent magnet synchronous motor(PMSM) drive without high frequency signal injection or special PWM pattern. This approach is based on the d-axis current regulator output voltage of the drive system which has the information of rotor position error. The rotor velocity can be estimated through a rotor position tracking PI controller that controls the position error to zero. For zero and low speed operation, the PI gains of rotor position tracking controller have a variable structure. The PI tuning formulas are derived by analyzing this control system using the frequency domain specifications such as phase margin and bandwidth assignment.

• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.1137-1139
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• 2002

In this paper, recurrent artificial neural network (RNN) based self tuning speed controller is proposed for the high performance drives of induction motor. RNN provides a nonlinear modeling of motor drive system and could give the information of the load variation, system noise and parameter variation of induction motor to the controller through the on-line estimated weights of corresponding RNN. Thus, proposed self tuning controller can change gains of the controller according to system conditions. The gain is composed with the weights of RNN. For the on-line estimation of the weights of RNN, extended kalman filter (EKF) algorithm is used. Self tuning controller that is adequate for the speed control of induction motor is designed. The availability of the proposed controller is verified through the MATLAB simulation with the comparison of conventional PI controller.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.2 s.314
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• pp.24-31
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• 2007

This paper presents self tuning PI(STPI) controller of IPMSM drive using neural network. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, STPI controller proposes a new method based neural network. STPI controller is developed to minimize overshoot, rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.223-229
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• 2023

This Paper propose the method that measure main system parameters for PI(proportional-integral) current control of a.c. motor adopting the vector control technique. For current control, the PI control input is could be tuning by several selective methods. Among the several methods, the method that using the main system parameters, wire resistance and inductance, are frequently used. In this study, the technique to dissect and measure these two system parameters through the results of simple feedback control. This analytic measurement method is measuring parameters step by step dissecting the results of P control using simple proportional feedback gain about the unit step or multiple step reference command. This strategy is an real time analytic measurement method that calculate current control gains of torque component and flux component both for vector control of A.C. motor without introducing the further measurement circuits and complex measuring algorithms.

• Proceedings of the KIEE Conference
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• 2005.04a
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• pp.191-193
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• 2005

This paper presents Hybrid PI controller of IPMSM drive using fuzzy adaptive mechanism(FAM) control. To increase the robustness, fixed gam PI controller, Hybrid PI controller proposes a new method based self tuning PI controller. Hybrid PI controller is developed to minimize overshoot and settling time following sudden parameter changes such as speed, load torque, inertia, rotor resistance and self inductance. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Journal of Advanced Navigation Technology
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• v.16 no.4
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• pp.703-708
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• 2012

This paper proposes the improvement of control performance in the wheeled inverted mobile robot system. and describes the modeling of a wheeled inverted pendulum type mobile robot driven by two different wheels for the position and velocity control. The system is sensitive on the parameter variation, therefore control signal should change to maintain desired state of the system in every instant. we designed proportional-plus-integral controller for our system, After linearization, the system was still unstable, throughout stability analysis of the system, we designed the values of the gains of a proportional-plus-integral controller. From the experimental results, we can find that the performance of the proposed method is better than of the manual tuning method.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.8
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• pp.1299-1312
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• 2004

This paper suggests the simple form of a fuzzy PID controller and describes the design principle, tracking performance, stability analysis and changes of parameters of a suggested fuzzy PID controller. A fuzzy PID controller is derived from the design procedure of fuzzy control. It is well known that a fuzzy PID controller has a simple structure of the conventional PID controller but posses its self-tuning control capability and the gains of a fuzzy PID controller become nonlinear functions of the inputs. Nonlinear calculation during fuzzification, defuzzification and the fuzzy inference require more time in computation. To increase the applicability of a fuzzy PID controller to digital computer, a simple form of a fuzzy PID controller is introduced by the backward difference mapping and the analysis of the fuzzy input space. To guarantee the BIBO stability of a suggested fuzzy PID controller, ‘small gain theorem’ which proves the BIBO stability of a fuzzy PI and a fuzzy PD controller is used. After a detailed stability analysis using ‘small gain theorem’, from which a simple and practical method to decide the parameters of a fuzzy PID controller is derived. Through the computer simulations for the linear and nonlinear plants, the performance of a suggested fuzzy PID controller will be assured and the variation of the gains of a fuzzy PID controller will be investigated.

• The Transactions of the Korean Institute of Power Electronics
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• v.22 no.1
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• pp.1-8
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• 2017

The optimal gain design method of a three-phase boost converter is proposed in this study. The control system has a two-loop configuration, in which each controller is coupled closely; thus, the optimal design is difficult to achieve using conventional gain-tuning method. The proposed method is adopted to the MATLAB SISO TOOL software and is based on the controller requirements, which are phase margin and cut-off frequency of the open-loop system. The optimal proportional -integral gains can be designed easily using the proposed interactive method of the SISO TOOL. The performance of the proposed system is verified through simulation and experiments.

• Proceedings of the KIEE Conference
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• 2006.10d
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• pp.53-55
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• 2006

This paper presents self tuning PI controller of IPMSM drive using neural network. NNPI controller is developed to minimize overshoot, rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.