• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1997.10a
• /
• pp.185-188
• /
• 1997

In this paper, a fuzzy precompensated PI controller for inverter-type air-conditioner is presented. The presented control scheme is composed of a fuzzy logic precompensator and PI controller, in which two control schemes are serially connected. The rules of the fuzzy precompensator is designed to improve the performance by considering the nonlinear characteristics of a temperature dynamics. The experimental results show the effectiveness of the proposed controller.

• Proceedings of the KIEE Conference
• /
• 1989.11a
• /
• pp.29-32
• /
• 1989

This paper proposes new position control method for DC servo motor by PI controller adopting sliding mode control. By adding sliding mode controller to conventional PI controller good robustness is obtained with good transient response and no steady state error which are merits in PI controller. In order to use microprocessor for digital control the principles of sliding mode control conventionally explained in continous-time system are extended to discrete-time system.

• Proceedings of the KIPE Conference
• /
• 1998.07a
• /
• pp.230-233
• /
• 1998

The conventional PI controller are fragile in parameter variation and load-variation. Therefore, in this paper, a speed control algorithm based on the Fuzzy PI controller is proposed for the high performance speed control of a voltage-source inverter to drive 3-phase induction motors. The computer simulation results show that the proposed controller are more excellent control characteristics than conventional PI controller in transient-state and steady-state response.

• Journal of Power Electronics
• /
• v.18 no.3
• /
• pp.723-735
• /
• 2018

Linear proportional-integral (PI) controllers are an attractive choice for controlling the speed of induction machines because of their simplicity and ease of implementation. Fractional-order PI (FO-PI) controllers, however, perform better than PI controllers because of their nonlinear nature and the underlying iso-damping property of fractional-order operators. In this work, an FO-PI controller based on the proposed first-order plus dead-time induction motor model and integer-order (IO) controllers, such as Ziegler-Nichols PI, Cohen-Coon PI, and a PI controller tuned via trial-and-error method, is designed. Simulation and experimental investigation on an indirect field-oriented induction motor drive system proves that the proposed FO-PI controller has better speed tracking, lesser settling time, better disturbance rejection, and lower speed tracking error compared with linear IO-PI controllers. Our experimental study also validates that the FO-PI controller maximizes the torque per ampere output of the induction machine and can effectively control the motor at low speed, in field-weakening regions, and under detuned conditions.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.11 no.6
• /
• pp.1176-1181
• /
• 2007

In this paper, we consider one of robust control system, fuzzy PI+fuzzy D controller dealing with noise, load, changed parameters of plant. We apply PI+D controller with a design for output of differential function and, we plan fuzzy controller with input for PID parameter of PI+D controller so We design control system meet with the change of environment with robust in relation to change of parameter. Fuzzy control is possessed of easy 4 rules and membership function and We design fuzzy PI+fuzzy D controller. Plant of this paper make a choice of 3 phase induction motor.

• The Transactions of the Korean Institute of Electrical Engineers B
• /
• v.54 no.8
• /
• pp.395-401
• /
• 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.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.28 no.2
• /
• pp.9-18
• /
• 2014

This paper proposes the fuzzy PI controller for maximum power point tracking(MPPT) control of photovoltaic system. The output characteristics of the solar cell are a nonlinear and affected by a temperature, the solar radiation. The MPPT control is a very important technique in order to increase an output and efficiency of the photovoltaic system. The conventional perturbation and observation(PO) and incremental conductance(IC) are the method which finding maximum power point(MPP) by the continued self-excitation vibration, and uses the fixed step size. If the fixed step size is a large, the tracking speed of maximum power point is faster, but the tracking accuracy in the steady state is decreased. On the contrary, when the fixed step size is a small, the tracking accuracy is increased and the tracking speed is slower. Therefore, this paper proposes the MPPT control using the fuzzy PI controller that can be improve a MPPT control performance. The fuzzy PI controller is adjusted a input of PI controller by fuzzy control and compensated a cumulative error of fuzzy control by PI controller. The fuzzy PI MPPT control is compared to conventional PO and IC MPPT method for various temperature and radiation condition. This paper proves the validity of the fuzzy PI controller using these results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2007.06a
• /
• pp.713-716
• /
• 2007

In this paper, we consider one of robust control system, fuzzy PI+fuzzy D controller dealing with noise, load, changed parameters of plant. We apply PI+D controller with a design for output of differential function and, we plan fuzzy controller with input for PID parameter of PI+D controller so We design control system meet with the change of environment with robust in relation to change of parameter. Fuzzy control is possessed of easy 4 rules and membership function and We design fuzzy PI+fuzzy D controller. Plant of this paper make a choice of 3 phase induction motor.

• The KSFM Journal of Fluid Machinery
• /
• v.18 no.1
• /
• pp.59-64
• /
• 2015

The pitch control system in wind turbines becomes more and more important as the wind turbines are larger in multi-MW size. PI controller has been applied in most pitch controllers and it has been known that gain-scheduling is essential for pitch control of wind turbines. A demo model of 2 MW wind turbine which represents the whole dynamics of wind turbine including dynamic behaviors of blade, tower and rotational shaft is given in the commercial Bladed S/W for real wind turbines. In this paper, some results on step responses of the pitch PI controller and effectiveness of gain-scheduled pitch PI controller are presented through the Bladed S/W for the 2 MW wind turbine.

• Proceedings of the KIEE Conference
• /
• 2004.10a
• /
• pp.182-184
• /
• 2004

This paper presents a PI controller tuning method for high performance permanent magnet synchronous motor (PMSM) drives under load variations using clustering fuzzy algorithm. In many speed tracking control systems PI controller has been used due to its simple structure and easy of design. PI controller, however, suffers from the electrical machine parameter variations and disturbances. In order to improve the tracking control performance under load variations, the PI controller parameters are modified during operation by clustering fuzzy method. This method based on optimal fuzzy logic system has simple structure and computational simplicity. It needs only sample data which is obtained by optimal controller off-line. As the sample data implemented in the adaptive fuzzy system can be modified or extended, a flexible control system can be obtained Simulation results show the usefulness of the proposed controller.