• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.152-154
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• 2006

This paper investigates the adaptive control of a fuzzy logic based speed and flux controller for a vector controlled induction motor drive. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive fuzzy controller is evaluated by simulation for various operating conditions. The validity of the proposed adaptive fuzzy controller is confirmed by performance results for induction motor drive system

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제55권2호
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• pp.98-106
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• 2006

This paper proposes to position and speed control of interior Permanent magnet synchronous motor(IPMSM) drive without mechanical sensor. Also, this paper develops a adaptive fuzzy controller based fuzzy logic control for high performance of PMSM drives. In the proposed system, fuzzy control is used to implement the direct controller as well as the adaptation mechanism. A Gopinath observer is used for the mechanical state estimation of the motor. The observer was developed based on nonlinear model of IPMSM, that employs a d-q rotating reference frame attached to the rotor. A Gopinath observer is implemented to compute the speed and position feedback signal. The validity of the proposed scheme is confirmed by various response characteristics.

• 한국지능시스템학회논문지
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• 제12권3호
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• pp.246-254
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• 2002

In this paper auto-tuning scheme of PID controller based on the reference model has been studied for a Process control system by immune algorithm. Up to this time, many sophisticated tuning algorithms have been tried in order to improve the PID controller performance under such difficult conditions. Also, a number of approaches have been proposed to implement mixed control structures that combine a PID controller with fuzzy logic. However, in the actual plant, they are manually tuned through a trial and error procedure, and the derivative action is switched off. Therefore, it is difficult to tune. Since the immune system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (Parallel Distributed Processing) network to complete patterns against the environmental situation. Simulation results reveal that reference model basd tuning by immune network suggested in this paper is an effective approach to search for optimal or near optimal process control.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2254-2259
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• 2005

In this paper, we propose a wavelet based fuzzy neural network(WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges advantages of neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of mobile robot using the gradient descent(GD) method. In addition, an approach that uses adaptive learning rates for the training of WFNN controller is driven via a Lyapunov stability analysis to guarantee the fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control performance of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• Journal of Advanced Marine Engineering and Technology
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• 제28권6호
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• pp.946-961
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• 2004

A new adaptive controller which combines a model reference adaptive controller (MRAC) and a fuzzy controller is developed for unstable nonlinear time-invariant systems. The fuzzy controller is used to analyze and to compensate the nonlinear time-invariant characteristics of the plant. The MRAC is applied to control the linear time-invariant subsystem of the unknown plant, where the nonlinear time-invariant plant is supposed to comprise a nonlinear time-invariant subsystem and a linear time-invariant subsystem. The stability analysis for the overall system is discussed in view of global asymptotic stability. In conclusion. the unknown nonlinear time-invariant plant can be controlled by the new adaptive control theory such that the output error of the given plant converges to zero asymptotically.

• 조명전기설비학회논문지
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• 제25권9호
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• pp.33-43
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• 2011

Since the fuzzy neural network(FNN) is universal approximators, the development of FNN control systems have also grown rapidly to deal with non-linearities and uncertainties. However, the major drawback of the existing FNNs is that their processor is limited to static problems due to their feedforward network structure. This paper proposes the recurrent FNN(RFNN) for high performance and robust control of SynRM. RFNN is applied to speed controller for SynRM drive and model reference adaptive fuzzy controller(MFC) that combine adaptive fuzzy learning controller(AFLC) and fuzzy logic control(FLC), is applied to current controller. Also, this paper proposes speed estimation algorithm using artificial neural network(ANN). The proposed method is analyzed and compared to conventional PI and FNN controller in various operating condition such as parameter variation, steady and transient states etc.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1998년도 Proceedings ICPE 98 1998 International Conference on Power Electronics
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• pp.11-16
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• 1998

In this paper, simultaneous estimation of rotor speed and time constant for a voltage source inverter (VSI) fed induction motor drive are disccussed. The theory is based on the Model Reference Adaptive System (MRAS). The identifier executes Simultaneous rotor speed and time constant so that vector control of the induction may be achieved in the rotor-flux oriented reference frame. Furthermore, to eliminate the offset error caused by the change in the stator resistance, a fuzzy resistance regulator is also designed which operates in parallel with the rotor speed and time constant identifier

• International Journal of Control, Automation, and Systems
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• 제5권3호
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• pp.317-323
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• 2007

Quality of Service (QoS) in the Internet depends on queuing and sophisticated scheduling in routers. In this paper, we address the issue of managing traffic flows with different priorities. In our reference model, incoming packets are first classified based on their priority, placed into different queues with different capacities, and then multiplexed onto one router link. The fuzzy nature of the information on Internet traffic makes this problem particularly suited to fuzzy methodologies. We propose a new solution that employs a fuzzy inference system to dynamically and efficiently schedule these priority queues. The fuzzy rules are derived to minimize the selected Lyapunov function. Simulation experiments show that the proposed fuzzy scheduling algorithm outperforms the popular Weighted Round Robin (WRR) queue scheduling mechanism.

• 조명전기설비학회논문지
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• 제29권2호
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• pp.27-32
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• 2015

A maximum power point tracking (MPPT) algorithm using fuzzy controller was considered. MPPT method was implemented based on the voltage and reference PV voltage value was obtained from Artificial Neural Network (ANN)-model of PV modules. Therefore, measuring only the PV module voltage is adequate for MPPT operation. Fuzzy controller is used to directly control dc-dc buck converter. The simulation results have been used to verify the effectiveness of the algorithm. The proposed method is compared with conventional PO(perturbation & observation), IC(Incremental Conductance) method. The nonlinearity and adaptiveness of fuzzy controller provided good performance under parameter variations such as solar irradiation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.248-252
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• 2003

This paper has a control method proposed for the effective self-tuning fuzzy speed control based on neural network of the induction motor indirect vector control. The vector control of an induction motor provides the decoupled control of the rotor flux magnitude and the torque producing current to performance is desirable. But, the drive performance often degrades for the machine parameter variations and its condition give rise to coupling of flux and torque current. The fuzzy speed control of an induction motor has the robustness about machine parameter variations compared with conventional PID speed control in a way. That proved to be some waf from the true. The purpose of this paper is to improve the adaptation by offering self-turning function to fuzzy speed controller. In this paper, the adaptive mechanism of fuzzy speed control in used ANN(Artificial Neural Network) technique is applied in an IFO induction machine drive, such that the machine can follow a reference model (an ideal field oriented machine) to achieve desired speed. In this paper proved the self-turning method of fuzzy controller has the robustness about parameter variation and the wide range of adaptation by simulation.