• Journal of the Semiconductor & Display Technology
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• v.13 no.4
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• pp.71-76
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• 2014

This paper presents the implementation for the BLDC motor speed control system using VHDL and FPGA. The BLDC motor is widely used in automation for its good robustness and easy controllability. In order to control the speed of the BLDC motor, the PI controller used for static RPM output of the BLDC motor to variations in load. In addition, by using the DA converter, we were able to monitor the BLDC motor reference speed and the current speed through real time. The motor speed command and the parameters of the PI speed controller were modified easily by the FPGA and the AD converter. Finally, in order to show the feasibility of the control algorithm the speed control characteristics of the motor was monitored using an oscilloscope and the DA converter. Further, the speed control system was designed in this paper has shown the applicability of the drive system of the factory automation.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.162-164
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• 1994

This paper proposes a servo control system of brushless motor at a low and high speed range. The control system is composed of the PI controller for high-speed control and the modified PI controller for low-speed control and the current controller using the hysteresis current control PWM method. The speed control performance is shown by the computer simulation.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2000.05a
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• pp.6-10
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• 2000

In this paper a methodology for designing a controller based on inverse dynamics for speed control of DC motors is presented. The proposed controller consists of a low-pass prefilter the inverse dynamic model of a system and the PI controller. The low-pass prefilter prevents high frequency effects from the inverse dynamic model. The model is characterized by a nonlinear friction model. The PI controller regulates the error between the set-point and the system output which is caused by modeling error disturbances and variations f parameters. The parameters of the model and the PI controller are optimized offlinely by genetic algorithm. The experimental results on a DC motor system illustrate the performance of the proposed controller.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2111-2113
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• 2002

This paper proposes the design of PI controller using real-coding genetic algorithm showing a good performance on convergence velocity and diversity of population among evolutionary computations. To evaluate the proposed method's effectiveness, we apply the proposed GA-PI controller to the speed control of an actual DC servomotor system. The experimental results show that GA-PI controller has the better control performance than PI controller in terms of settling time rising time and overshoot.

• The Transactions of the Korean Institute of Power Electronics
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• v.22 no.1
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• pp.60-66
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• 2017

This paper presents the design of a proportional-integral (PI)-proportional-derivative (PD) position controller without using a speed controller in motor drive systems. Unlike the existing PI-PD position controller design methods, the proposed controller is designed by reducing the entire position control system to a second-order transfer function. Thus, the gain values for the PI-PD position controller can be determined easily by a given bandwidth of the position controller. The PI-PD position controller designed by the proposed method is adopted for position control in an interior permanent magnet synchronous motor drive system to confirm the validity of the proposed design method. The effectiveness of the proposed design method is confirmed through experiments.

• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.1453-1455
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• 2005

This paper presents self tuning PI controller of IPMSM drive using neural network. Self tuning PI 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.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1168-1172
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• 2004

This paper presents a speed controller based on an adaptive fuzzy algorithm for high performance permanent magnet synchronous motor (PMSM) drives under parameter and load variations. 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 adaptive 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.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.1142-1144
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• 2003

This Paper presents a speed controller based on the adaptive fuzzy tuning method for brushless DC(BLDC) motor drives under load variations. Generally, the speed tracking control systems use PI controller 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, PI controller of which the parameters are modified during operation by adaptive fuzzy tuning 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.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.541-543
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• 1998

Generally, PI control is simple and easy to implement and gains of PI control are determined by specifying a dynamics of the servo driver system. However, the gain-tuning is so difficult that it is relied on an expert's effort. This paper presents a gain auto-tuning method for PI controllers based on a fuzzy inference mechanism. First, the proposed fuzzy inference system identifies a system moment of inertia and adjusts control gains by using the difference in speed responses between a real plant and a reference model. Second, this paper proposes an improved fuzzy PI controller. To reduce the speed overshoot, we adapt a control method that selects a proper PI gains with respect to the load inertia variation. To prove the validity of the proposed gain tuning algorithm and the feasibility of the servo drive, a high performance servo drive will be implemented by DSP(TMS320C31) and intelligent power module (IPM). The proposed controller is applied to the speed control of the 300W AC servo motor. Some simulations and experimental results show that the proposed fuzzy PI controller is more robust than the conventional PI controller against the load inertia variation.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.1027-1028
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• 2006

In this paper, design of current and speed controller for DC motor drive system using Embedded Target for TI C2000DSP in Matlab/Simulink is introduced. Current and speed controller is designed and implemented using program simply and easily, and speed control response of DC motor can be advanced. Current and speed control of DC motor is carried in eZdsp F2812 control board using Embeded Target for TI C2000DSP in Matlab/Simulink. Speed feedback is processed through A/D converter using tacho generator as speed sensor, and current feedback is processed through A/D converter using hall sensor as current sensor. Controller is designed to PI current controller and PI speed controller. Current and speed response is verified through simulations and experiments.