• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.481-486
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• 2018

DC motors are classified as DC motors with brush structure and BLDC motors without brush structure. Representing the speed control of the BLDC motor is the PI control. The speed control using the PI controller has a disadvantage that the response characteristic to reach the steady state is slow. Therefore in this paper, a voltage controlled speed controller using a Fuzzy Logic Controller (FLC), which has a short steady response time and usefulness of nonlinear control. The validity and usefulness of the proposed fuzzy speed controller are verified by simulation through Simulink of MATLAB program. Experiments were performed on the PI controller and the proposed fuzzy speed controller in three cases with reference speeds of 500rpm, 800rpm, and 1500rpm. Experimental results show that the proposed fuzzy controller has more 30% improved steady state speed response than PI controller.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.8
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• pp.1405-1411
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• 2007

In this paper, a new speed sensorless control based on a fuzzy compensator are proposed for the interior permanent magnet synchronous motor (IPMSM) drives. The conventional proportional plus integrate(PI) control are very sensitive to step change of the command speed, parameter variations and load disturbance. To cope with these problems of the PI control, the estimated speeds are compensated by using the fuzzy logic controller (FLC). In the FLC used by the speed compensator of the IPMSM, the system control parameters are adjusted by the fuzzy rule based system, which is a logical model of the human behavior for process control. The effectiveness of algorithm is confirmed by the experiments.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2006.05a
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• pp.309-314
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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. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current md 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 adaptive teaming fuzzy neural network and artificial neural network. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed control of IPMSM using adaptive teaming fuzzy neural network and estimation of speed using artificial neural network. 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 adaptive teaming fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive teaming fuzzy neural network and artificial neural network.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.676-680
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• 1991

This paper proposes fuzzy control algorithms for a DC moter speed control. The proposed algorithms are constructed by the fuzzy controller and the fuzzy compensator. The fuzzy compensator used to overcome rapidly the effects caused by the disturbance and is mounted outside of the closed loop of the fuzzy controller. The fuzzy control rules are established from human operator experience and basic engineering knowledge about the process dynamics. Simulation results show that the proposed algorithms compensate for parameter variation and disturbance.

• International Journal of Air-Conditioning and Refrigeration
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• v.15 no.2
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• pp.61-69
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• 2007

This paper deals with fuzzy control with a feedforward compensator to progress both energy saving and coefficient of performance (COP) in a variable speed refrigeration system. Both the capacity and superheat are controlled simultaneously and independently in the system. By adopting the fuzzy theory, the controller design for the capacity and superheat is possible without depending on a dynamic model of the system. Moreover, the feedforward compensator of the superheat can reduce influence of the interfering loop between the capacity and superheat. Some experiments are conducted to design appropriate fuzzy controller by an iteration manner. The results show that the proposed fuzzy controller with the compensator can establish good control performances for the complicated refrigeration system in spite of its inherent strong non-linearity. Also, the fuzzy control performances were investigated by comparing to the model based PI control experimental results to evaluate transient behavior under the control.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2000.05a
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• pp.1-5
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• 2000

The conventional PID controller has been extensively used to speed control of marine diesel engines. However one of drawbacks is that its control performance can be degraded if the parameters are fixed on whole operating points. In this paper a scheme for integrating PID control and the fuzzy technique is presented to control speed of a marine diesel engine on whole operating points. At first the PID controller is designed at each speed mode whose parameters are optimally adjusted using a genetic algorithm, Then fuzzy "if-then" rules combine the controllers as a consequence part. To demonstrate the effectiveness of the proposed fuzzy controller a set of simulation works on a marine diesel engine are carried out.rried out.

• Journal of Power Electronics
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• v.12 no.6
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• pp.904-911
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• 2012

This paper presents a fuzzy adaptive speed controller that guarantees a fast dynamic behavior and a precise trajectory tracking capability for surfaced-mounted permanent magnet synchronous motors (SPMSMs). The proposed fuzzy adaptive control strategy is simple and easy to implement. In addition, the proposed speed controller is very robust to system parameter and load torque variations because it does not require any accurate parameter values. The global stability of the proposed control system is analytically verified. To evaluate the proposed fuzzy adaptive speed controller, both simulation and experimental results are shown under motor parameter and load torque variations on a prototype SPMSM drive system.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.480-480
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• 2000

In this paper, we proposed a fuzzy-neuro controller to control the speed of wound rotor induction motor with slip energy recovery. The speed is limited at some range of sub-synchronous speed of the rotating magnetic field. Control speed by adjusting resistance value in the rotor circuit that occurs the efficiency of power are reduced, because of the slip energy is lost when it passes through the rotor resistance. The control system is designed to maintain efficiency of motor. Recently, the emergence of artificial neural networks has made it conductive to integrate fuzzy controllers and neural models for the development of fuzzy control systems, Fuzzy-neuro controller has been designed by integrating two neural network models with a basic fuzzy logic controller. Using the back propagation algorithm, the first neural network is trained as a plant emulator and the second neural network is used as a compensator for the basic fuzzy controller to improve its performance on-line. The function of the neural network plant emulator is to provide the correct error signal at the output of the neural fuzzy compensator without the need for any mathematical modeling of the plant. The difficulty of fine-tuning the scale factors and formulating the correct control rules in a basic fuzzy controller may be reduced using the proposed scheme. The scheme is applied to the control speed of a wound rotor induction motor process. The control system is designed to maintain efficiency of motor and compensate power factor of system. That is: the proposed controller gives the controlled system by keeping the speed constant and the good transient response without overshoot can be obtained.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.04a
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• pp.354-358
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• 1992

This paper describes a development of high speed fuzzy inference circuit for the industrialprocesses. The hardware fuzzy inference circuit is developed utilizing a hardware fuzzy inference circuit is developed utilizing a DSP and a multiplier and accumulator chip. To enhance the inference speed, the pipeline disign is adopted at the bottleneck and the general Max-Min inference method is slightly modified as Max-max method. As a results, the inference speed is evaluated to be 100 KFLIPS. Owing to this high speed feature, satisfactory application can be attained for complex high speed motion control as well as the control of multi-input multi-output nonlinear system. As an application, the developed fuzzy inference circuit is embedded to a PLC (Porgrammable Logic Controller) for industrial process control. For the fuzzy PLC system, to fascilitate the design of the fuzzy control knowledge such as membership functions, rules, etc., a MS-Windows based GUI (Graphical User Interface) software is developed.

• Transactions of the Korean Society of Automotive Engineers
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• v.5 no.5
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• pp.37-50
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• 1997

The objective of this paper is to study on the idle speed control using the fuzzy logic controller under load disturbance. The design procedure for fuzzy logic controller depends on the expert's knowledge or trial and error. The inputs of the fuzzy controller are error of rpm and variation of rpm. The output of the fuzzy controller is an ISC motor step and ignition timing. The airflow is controlled by the ISC motor movement and the idle speed is controlled by the airflow control and ignition timing control. During the control, air to fuel was checked by LAMBDA sensor. All experiments were performed in a real vehicle.