• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.1
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• pp.122-129
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• 2008

The direct torque control method of an inverter fed squirrel cage typed induction motor using fuzzy logic controller has been proposed. This method is suitable for the traction which requires a fast torque response during the star-up and step change. The fuzzy control algorithm based upon the control principles of conventional DSC(Direct Self Controller) is developed. The fuzzy algorithm is tarried out by defuzzification strategy of the fuzzy output extracted from the possibility distribution of an inferred fuzzy control rule. The flux and torque of an induction motor are estimated by the dynamic model of the rotor flux field-oriented scheme which has decoupling characteristics and excellent dynamic response over a wide speed range. The proposed controller shows a good dynamic response. Moreover, since the fuzzy controller possesses highly adaptive capability, the performance of fuzzy controller is quite robust and insensitive to the motor parameters and change of operation conditions.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2378-2381
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• 2003

In recent years, fuzzy logic has received greater emphasis in the field of power electronics and motion control by virtue of its adaptive capability. A new fuzzy logic based soft-start scheme for induction motor drives close to load detection has been discussed here using microcontroller based thyristorised voltage controller. Rule based soft-start algorithm is fully realised through a software approach only. The soft-start strategy is based on the change of input impedance during starting period. The prototype has been tested under various loading conditions and found to be reliable.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.4
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• pp.191-202
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• 2013

A control structure that makes possible the integration of a kinematic controller and a fuzzy logic (FL) deadzone compensator for mobile robots is presented. A tuning algorithm is given for the fuzzy logic parameters, so that the deadzone compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The fuzzy logic deadzone compensator is implemented on a mobile robot to show its efficacy.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.834-837
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• 1993

This paper describes the implementation of an autonomous fuzzy logic controller. The controller is endowed with basic control principles and learning constructs which enable it to autonomously modify its control policy based on system performance. The controller lies dormant when system response is satisfactory but if rapidly initiates adaptation in real time when adverse performance is observed. The autonomous fuzzy controller is implemented on an Intel MCS-51 series micro-controller board using an inexpensive 8-bit Intel 8031 processor. The 11.06 MHz micro-controller operates at a rate exceeding 200 "global" look-up table reinforcements per second. This is important when developing practical on-line adaptive controllers for fast systems. It is also significant because an initial controller look-up table could be incorrect or non-existent. The relatively high learning rate enables the controller to learn to control a system even while it is controlling it.

• Journal of Intelligence and Information Systems
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• v.17 no.2
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• pp.1-22
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• 2011

In this paper, we propose an adaptive genetic algorithm (aGA) approach for effectively solving the sequencing problem with precedence constraints (SPPC). For effective representation of the SPPC in the aGA approach, a new representation procedure, called the topological sort-based representation procedure, is used. The proposed aGA approach has an adaptive scheme using a fuzzy logic controller and adaptively regulates the rate of the crossover operator during the genetic search process. Experimental results using various types of the SPPC show that the proposed aGA approach outperforms conventional competing approaches. Finally the proposed aGA approach can be a good alternative for locating optimal solutions or sequences for various types of the SPPC.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.151-157
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• 2013

This paper proposes an online adaptive fuzzy controller for a wind energy conversion system (WECS) that is intrinsically highly nonlinear plant. In real application, to obtain exact system parameters such as power coefficient, many measuring instruments and off-line implementations are required, which is very difficult to perform. This shortcoming can be avoided by introducing fuzzy system in the controller design in this paper. The proposed adaptive fuzzy control scheme using self-structuring algorithm requires no system parameters to meet control objectives. Even the structure of the fuzzy system is automatically grows on-line, which distinguishes our proposed algorithm over the previously proposed fuzzy control schemes. Combining derivative estimator for wind velocity, the whole closed-loop system is shown to be stable in the sense of Lyapunov.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.402-408
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• 2016

In this paper, a new approach to the terminal sliding mode control using adaptive fuzzy sliding surfaces is proposed. The idea behind this approach is to utilize an adaptive sliding surface, in which the slope of the surface is updated on line using a SISO fuzzy logic inference system. We expanded the concepts of terminal sliding mode controller and proposed the terminal sliding mode control input with continuous reaching laws. The computer simulation results have shown the improved performance of the proposed control approach in terms of a decrease in the reaching and settling times and chattering free as compared to the conventional terminal sliding mode control with a fixed sliding surface. The proposed controller has also an advantage that has less computational burden to the conventional terminal sliding mode control using one-directional fuzzy rules.

• Journal of Power Electronics
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• v.12 no.5
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• pp.800-812
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• 2012

This paper analyses the mathematical model and control strategies of a Hybrid Active Power Filter with Injection Circuit (IHAPF). The control strategy based on the load harmonic current detection is selected. A novel control method for a IHAPF, which is based on the analyzed control mathematical model, is proposed. It consists of two closed-control loops. The upper closed-control loop consists of a single fuzzy logic controller and the IHAPF model, while the lower closed-control loop is composed of an Adaptive Network based Fuzzy Inference System (ANFIS) controller, a Neural Generalized Predictive (NGP) regulator and the IHAPF model. The purpose of the lower closed-control loop is to improve the performance of the upper closed-control loop. When compared to other control methods, the simulation and experimental results show that the proposed control method has the advantages of a shorter response time, good online control and very effective harmonics reduction.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.221-223
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• 2000

This paper describes the design of SVC fuzzy logic controller (SVC-FLC) using adaptive evolutionary algorithm and we tuned the gain of input-output variables of SYC-FLC using it. We performed the nonlinear simulation on an single-machine infinite system to prove the efficiency of the proposed method. The proposed SYC-FLC showed the better performance than PD controller in terms of the settling time and damping effect, for system operation condition used in evaluating the robustness and three phase grounding default in cases of nominal loading used in tuning SVC-FLC for a single-machine infinite system.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.43-51
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• 1996

Fuzzy rules in fuzzy logic control play a major role in deciding the control dynamics of a fuzzy logic controller. Thus, control performance is mainly determined by the quality of fuzzy rules. This paper introduces an optimization method for fuzzy rules using GAS with adaptive probabilies of crossover and mutation. Also we design two fitness measures to satisfy control objectives by partitioning the response of a plant into two parts. An initial population is generated by an automatic fuzzy rule generation method instead of random selection for fast a.pproaching to the final solution. We employed a nonlinear plant to simulate our method. It is shown through simulation that our method is reasonable and can be useful for optimizing fuzzy rules.