• Title/Summary/Keyword: PI control gain

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The Stable Position Control of Hybrid type Linear Pulse Motor by Digital PI Control (디지털 PI 제어에 의한 HLPM의 안정된 위치제어)

  • Youn, Shin-Yong;Baek, Soo-Hyun;Kim, Yong;Kim, Cherl-Jin;Maeng, In-Jae
    • The Transactions of the Korean Institute of Electrical Engineers B
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    • v.49 no.10
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    • pp.637-645
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    • 2000
  • This study was represented the improvement of the flexible position control for linear motion of hybrid type linear pulse motor(HLPM). The driving method used a minute 125 microstep drive instead of full step drive method. The digital control method was applied to the PI control for more stable position control, at this time the PI control parameters have gained by a Ziegler-Nichols turning method. The loop transfer function of control system was combined with both motor transfer function and digital PI control equation. Such, the proper for digital PI control system is verified to through the simulation and experimental result of the stability step response and bode plot with proper gain and phase margin.

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Superheat Control of an Inverter-driven Heat Pump Using PI Control Algorithm

  • Park, Jong-Min;Kim, Yong-Chan
    • International Journal of Air-Conditioning and Refrigeration
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    • v.10 no.2
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    • pp.106-115
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    • 2002
  • The performance of an inverter-driven water-to-water heat pump with an electronic expansion valve (EEV) was measured as a function of compressor frequency, load conditions, and EEV opening. Based on the test results, a controller using proportional integral (PI) feedback or PI feedforward algorithm was designed and tested to investigate capacity modulation and transient response control of the system. Although the relation between superheat and EEV opening of the heat pump showed nonlinear characteristics, a control gain obtained at the rated frequency was applicable to various operating conditions without causing large deviations. When the simple PI feedback control algorithm was applied, a large overshoot of superheat and wet compression were observed due to time delay effects of compressor frequency. However, applying PI feedforward control scheme yielded better system performance and higher reliability, compared to the PI feedback algorithm.

Design of Fuzzy PI Controller for Variable Speed Drive of Switched Reluctance Motor (SRM의 가변속 구동을 위한 퍼지 PI 제어기 설계)

  • Yoon, Yong-Ho;Park, Jun-Suk;Song, Sang-Hoon;Won, Chung-Yuen;Kim, Jae-Moon
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.61 no.10
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    • pp.1529-1535
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    • 2012
  • This paper presents the application algorithm for speed control of Switched Reluctance Motor. The conventional PI controller has been widely used in industrial applications. But it is very difficult to find the optimal PI control gain. Fuzzy control does not need any model of plant. It is based on plant operator experience and heuristics. The proposed fuzzy logic modifier increases the control performance of conventional PI controller. Simulation and experimental results show that the proposed fuzzy control method was superior to the conventional PI controller in the respect of system performance. The experiments are performed to verify the capability of proposed control method on 6/4 salient type SRM.

Gain-phase margin specified PI speed control of a PM synchronous motor

  • Kim G.S.;Youn M.J.
    • Proceedings of the KIPE Conference
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    • 2003.07b
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    • pp.994-997
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    • 2003
  • Simple tuning formulae are derived to design a Pl controller to meet the gain and phase margin specifications. These formulae are suitable for the auto-tuning of a process where the robustness should be guaranteed. The auto-tuned PI controller is examined for the speed regulation of a PM synchronous motor.

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Anti-windup for Complex Vector Synchronous Frame PI Current Controller (복소 벡터 동기좌표계 비례 적분 전류 제어기의 안티와인드업 이득 설정)

  • Yoo, Hyun-Jae;Jeong, Yu-Seok;Sul, Seung-Ki
    • The Transactions of the Korean Institute of Power Electronics
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    • v.11 no.5
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    • pp.404-408
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    • 2006
  • This paper presents an anti-windup gain selection method for a complex vector synchronous frame PI current controller. The complex vector PI current controller is more robust to the parameter variation than the state feedback decoupling PI current controller. The complex vector PI current controller also includes an integral term, which can results in windup problem when the controller is saturated due to physical limitation of the system. Furthermore, even an anti-windup is utilized, inappropriate gain can deteriorate the performance of the current controller. Therefore, appropriate anti-windup gain selection method for a complex vector current controller has been proposed based on the mathematical description of the current control system. The superior performance of the current control system with the proposed anti-windup gain has been verified by the experimental results.

Improved BP-NN Controller of PMSM for Speed Regulation

  • Feng, Li-Jia;Joung, Gyu-Bum
    • International journal of advanced smart convergence
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    • v.10 no.2
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    • pp.175-186
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    • 2021
  • We have studied the speed regulation of the permanent magnet synchronous motor (PMSM) servo system in this paper. To optimize the PMSM servo system's speed-control performance with disturbances, a non-linear speed-control technique using a back-propagation neural network (BP-NN) algorithm forthe controller design of the PMSM speed loop is introduced. To solve the slow convergence speed and easy to fall into the local minimum problem of BP-NN, we develope an improved BP-NN control algorithm by limiting the range of neural network outputs of the proportional coefficient Kp, integral coefficient Ki of the controller, and add adaptive gain factor β, that is the internal gain correction ratio. Compared with the conventional PI control method, our improved BP-NN control algorithm makes the settling time faster without static error, overshoot or oscillation. Simulation comparisons have been made for our improved BP-NN control method and the conventional PI control method to verify the proposed method's effectiveness.

HBPI Controller of Induction Motor using Fuzzy Adaptive Mechanism (퍼지 적응 메카니즘을 이용한 유도전동기의 HBPI 제어기)

  • Nam Su-Myung;Lee Hong-Gyun;Chung Dong-Hwa
    • The Transactions of the Korean Institute of Electrical Engineers B
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    • v.54 no.8
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    • pp.395-401
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    • 2005
  • This paper presents Hybrid PI(HBPI) controller of induction motor drive using fuzzy control. In general, PI controllers used in computer numerically controlled machines process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, HBPI controller proposes a new method based self tuning PI controller. HBPI 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 induction motor are presented to show the effectiveness of the proposed gam 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.

An optimal scaling gain tuning method for designing a fuzzy logic controller (퍼지로직제어기를 설계하기 위한 최적 비율 이득 조정방법)

  • Shin, Hyunseok;Shim, Hansoo;Kwon, Cheol;Kang, Hyungjin;Park, Mignon
    • 제어로봇시스템학회:학술대회논문집
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    • 1996.10b
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    • pp.192-194
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    • 1996
  • This paper propose an optimal scaling gain tuning method of the fuzzy PI controller using Genetic Algorithm(GA). Scaling gains can reflect the control resolution and fuzziness of input/output variables. By the scaling gain method, the design of a fuzzy logic controller(FLC) can be simplified without affecting the system performance in comparison with multi-decision table method. In designing a fuzzy logic controller, the analytic approach method for the optimization is unavailable. Therefore GA is excellent optimization algorithms for scaling gain tuning. Using this optimal scaling gain tuning method, a good performance can be achieved both in transient and steady state.

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Measurement strategy of a system parameters for the PI current control of the A.C. motor (교류 전동기의 PI 전류제어를 위한 시스템 파라미터 계측법)

  • Jung-Keyng Choi
    • 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.

The Control of an Electrostrictive Polymer Actuator by Using Neural Network

  • Youn, Ji-Won;Jeon, Jae-Wook;Nam, Jae-Do;Park, Hyoukryeol;Kim, Hunmo
    • 제어로봇시스템학회:학술대회논문집
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    • 2002.10a
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    • pp.120.4-120
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    • 2002
  • $\textbullet$ In order to operate EP actuator, high voltage is applied to that. $\textbullet$ Our previous control algorithm for an EP actuator was PI method with constant gain. $\textbullet$ But this Control method is limitation such as rising time, steady-state error, and settling time. $\textbullet$ A neural network algorithm is proposed for improvement of performance. $\textbullet$ To do this, neural network algorithm changes the gain of PI control. $\textbullet$ In order to efficient drive EP actuator, the gain is changed at some point. $\textbullet$ Neural network method improve the performance of operation.

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