• Journal of Power Electronics
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• v.15 no.3
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• pp.730-740
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• 2015

This paper presents model reference adaptive system speed estimators based on Type-1 and Type-2 fuzzy logic controllers for the speed sensorless direct torque and flux control of an induction motor drive (IMD) using space vector pulse width modulation. A Type-1 fuzzy logic controller (T1FLC) based adaptation mechanism scheme is initially presented to achieve high performance sensorless drive in both transient as well as in steady-state conditions. However, the Type-1 fuzzy sets are certain and cannot work effectively when a higher degree of uncertainties occurs in the system, which can be caused by sudden changes in speed or different load disturbances and, process noise. Therefore, a new Type-2 FLC (T2FLC) - based adaptation mechanism scheme is proposed to better handle the higher degree of uncertainties, improve the performance, and is also robust to different load torque and sudden changes in speed conditions. The detailed performance of different adaptation mechanism schemes are performed in a MATLAB/Simulink environment with a speed sensor and sensorless modes of operation when an IMD is operates under different operating conditions, such as no-load, load, and sudden changes in speed. To validate the different control approaches, the system is also implemented on a real-time system, and adequate results are reported for its validation.

• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.6
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• pp.534-542
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• 2005

This paper proposes the fuzzy logic based vector control method for a Surface Mounted Permanent Magnet Synchronous Motor(SMPMSM) used in the elevators. The gain of a conventional PI speed controller in the elevator drive system can not be usually set high due to mechanical resonances, therefore its performance becomes deteriorated. There have been many methods to solve above problems such as an acceleration feedback in the speed controller. However, the above methods have defects that parameter information is demanded. In this paper', a Fuzzy controller(FC) is adopted in the elevator drive system. The performance of a fuzzy controller is compared with a PI controller in the no load and load conditions by simulation and experiments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.3
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• pp.223-231
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• 2006

In high-speed fuzzy control systems applied to intelligent systems such as robot control, one of the most important problems is the improvement of the execution speed of the fuzzy inference. In particular, it is more important to have high-speed operations in the consequent part and the defuzzification stage. To improve the speedup of fuzzy controllers for intelligent systems, this paper presents an integer line mapping algorithm to convert [0, 1] real values of the fuzzy membership functions in the consequent part to a $400{\times}30$ grid of integer values. In addition, this paper presents a method of eliminating the unnecessary operations of the zero items in the defuzzification stage. With this representation, a center of gravity method can be implemented with only integer additions and one integer division. The proposed system is analyzed in the air conditioner control system for execution speed and COG, and applied to the truck backer-upper control system. The proposed system shows a significant increase in speed as compared with conventional methods with minimal error; simulations indicate a speedup of an order of magnitude. This system can be applied to real-time high-speed intelligent systems such as robot arm control.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.11
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• pp.2474-2479
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• 2012

A sensorless vector control of an induction motor provides a good performance in the middle and high speed region. However, in the low speed region, it is very difficult to implement the sensorless vector controller because the feeding voltage measured by the motor is very low. In this paper, to improve the performance of a sensorless vector control of an induction motor in the low speed region under 3Hz, we proposed the fuzzy logic controller using the zooming algorithm. To verify the performance of the proposed controller, an experiment has been performed.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2008.05a
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• pp.440-444
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• 2008

The 7-phase BLDC motor is possible for higher efficiency per the unit area, high power and high speed due to the increasing number of phase. Also, it can be looking forward to reduce the current ripple at a point of commutation by the increasing number of phase. Thus, a study for applications of servo system, medical and military instruments is progressing about the BLDC motor is manufactured with multi-phase, currently. This paper is used the fuzzy logic control method for speed control of 7-phase BLDC motor and this is compared with the conventional PI controller using by simulation and experimental results for verification validity of the fuzzy logic controller in this system. The 7-phase BLDC motor and controller are modeled by PSIM6.0 software of PowerSim co. in simulation and we are experimented by the test board that is composed with TMS320VC33-150 DSP controller of Texas Instruments co. and FLEX EPF6016TC144-3 of ALTERA co.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2005.11a
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• pp.315-320
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• 2005

This paper is proposed adaptive fuzzy-neuro controller for high performance of induction motor drive. The design of this algorithm based on fuzzy-neural network controller that is implemented using fuzzy control and neural network. This controller uses fuzzy rule as training patterns of a neural network. Also, this controller uses the back-propagation method to adjust the weights between the neurons of neural network in order to minimize the error between the command output and actual output. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of nor measured between the motor speed and output of a reference model. The control performance of the adaptive fuzy-neuro controller is evaluated by analysis for various operating conditions. The results of experiment prove that the proposed control system has strong high performance and robustness to parameter variation, and steady-state accuracy and transient response.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.55 no.2
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• pp.89-97
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using learning mechanism-fuzzy neural network(LM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_{d}$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using LM-FNN controller and ANN controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of IPMSM using LM-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the LM-FNN and ANN controller.

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

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2849-2852
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• 1999

This paper describes the fuzzy PD controller using simplified indirect inference method. First, the fuzzy PD controller is derived from the conventional continuous time linear PD controller. Then the fuzzification, control-rule base, and defuzzification using SIIM in the design of the fuzzy controller are discussed in detail. The resulting controller is a discrete time fuzzy version of the conventional PD controller. which has the same linear structure. but are nonlinear functions of the input signals. The proposed controller enhances the self-tuning control capability. particularly when the process to be controlled is nonlinear. As the SIIM is applied, the fuzzy Inference results can be calculated with splitting fuzzy variables into each action component and are determined as the functional form of corresponding variables. So the Proposed method has the capability of the high speed inference and extending the fuzzy input variables easily. Computer simulation results have demonstrated the superior to the control Performance of the one Proposed by D. Misir et at. Final)y. we simulated the contour control of the X-Y tables with direct control strategies using the proposed fuzzy PD 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.