• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.11
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• pp.17-25
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• 2014

This paper proposes a novel maximum power point tracking (MPPT) system using a fuzzy logic control (FLC) algorithm for robust in-environment changing. The power available at the output of a photovoltaic (PV) cell continues to change with radiation and temperature because a solar cell exhibits nonlinear current-voltage characteristics. Therefore, the maximum power point (MPP) of PV cells varies with radiation and temperature. The MPPT methods are used in PV systems to make full utilization of the PV array output power, which depends on radiation and temperature. The conventional MPPT control methods such as constant voltage (CV), perturbation and observation (PO) and incremental conductance (IC) have been studied but these methods are problematic in that they fail to take into account the changing environment. The proposed FLC controller is based on the fuzzy control algorithm and facilitates robust control with the environmental changes. Also, the PV systems applied FLC controller is modeled by PSIM and the response characteristics of the FLC method according to environmental variations are analyzed through comparison with the performance of conventional methods. The validity of this controller is shown through response results.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1483-1486
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• 2002

In this paper, a new scheme of iterative loaming control of a robot manipulator is presented. The proposed method uses a fuzzy sliding mode controller(FSMC), which is designed based on the similarity between the fuzzy logic control(FLC) and the sliding mode control(SMC), for the feedback. With this, the proposed method makes possible fDr fast iteration and has advantages that no linear approximation is used for the derivation of the learning law or in the stability proof Full proof of the convergence of the fuzzy sliding base learning scheme Is given.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.737-741
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• 1993

In this paper, we design the fuzzy logic controller(FLC) for a nonlinear multivariable steam generating unit. Based on the knowledges of operator, the self-organizing controller(SOC) - a kind of FLC - is developed and tested. Both FLC and SOC based on linguistic rules have the advantages of not needing of some exact mathematical model for plant to be controlled. Beside, the SOC modifies the existing control rules by monitoring the control performance. The computer simulations have been carried out for the 200MW steam generating unit to show the usefulness of the proposed method and the effects of disturbances and parameter variations are considered.

• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.231-242
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• 2023

A novel combinatorial type-2 adaptive neuro-fuzzy inference system (T2-ANFIS) and robust proportional integral derivative (PID) control framework for intelligent vibration mitigation of uncertain structural system is introduced. The fuzzy logic controllers (FLCs), are designed independently of the mathematical model of the system. The type-1 FLCs, have a limited ability to reduce the effect of uncertainty, due to their fuzzy sets with a crisp degree of membership. In real applications, the consequent part of the fuzzy rules is uncertain. The type-2 FLCs, are robust to the fuzzy rules and the process parameters due to the fuzzy degree of membership functions and footprint of uncertainty (FOU). The adaptivity of the proposed method is provided with the optimum tuning of the parameters using the neural network training algorithms. In our approach, the PID control force is obtained using the generalized type-2 neuro-fuzzy in such a way that the stability and robustness of the controller are guaranteed. The robust performance and stability of the presented framework are demonstrated in a numerical study for an eleven-story seismically-excited building structure combined with an active tuned mass damper (ATMD). The results indicate that the introduced type-2 neuro-fuzzy PID control scheme is effective to attenuate plant states in the presence of the structured and unstructured uncertainties, compared to the conventional, type-1 FLC, type-2 FLC, and type-1 neuro-fuzzy PID controllers.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.785-788
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• 1993

This paper presents the prototype framework for automated integration of CMOS current-mode fuzzy logic circuits using an intelligent module approach. The library of modules representing the standard fuzzy logic operators was built. These modules were finally used to synthesized sophisticated fuzzy logic units. Fuzzy unit designs were made based upon the results of a newel methodology of the current mirror-based fuzzy logic function synthesis. This methodology is actually incorporated into the presented framework. As an example, the membership function unit was synthesized, simulated, and the final layout was generated using the presented framework. Finally, the fuzzy logic controller unit (FLC) was generated using the proposed framework. Simulation as well as measurement results show unquestionable advantages of the proposed fuzzy logic function integration system over the classical design methodology with respect to the area, relative error and performance.

• Journal of Power Electronics
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• v.6 no.3
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• pp.226-234
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• 2006

Photovoltaic (PV) generators have nonlinear V-I characteristics and maximum power points which vary with illumination level and temperature. Using a maximum power point tracker (MPPT) with an intermediate converter can increase the system efficiency by matching the PV systems to the load. This paper presents a maximum power point tracker based on fuzzy logic and a control scheme for a single-phase inverter connected to the utility grid. The fuzzy logic controller (FLC) provides an adaptive nature for system performance. Also the FLC provides excellent features such as fast response, good performance and the ability to change the fuzzy parameters to improve the control system. A single-phase AC-DC inverter is used to connect the PV system to the grid utility and local loads. While a control scheme is implemented to inject the PV output power to the utility grid at unity power factor and reduced harmonic level. The simulation results have shown the effectiveness of the proposed scheme.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.3
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• pp.21-33
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• 1997

In this paper, a method of self-organizing for fuzzy logic controller(FLC) through learning of the proper direction of coritrol is proposed. In case of designing a self-organizing FLC for unknown dynamic plants based on the gradient descent method, it is difficult to identify the desirable direction of the change of control inpul. in which the error would be decreased. To resolve this problem, we propose a method as fo1lows:at first, assign representative values for the direction of change of error with respect to control input to each partitioned region of the states, and then, learn the fuzzy control rules using the reinforced representative values through iterative trials. 'The proposed self-organizing FLC has simple structure and it is easy to design. The validity of the proposed method is proved by the computer simulation for an inverted pendulum system.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.209-216
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• 2005

The controller that can control the smart base isolation system consisting of M damper and friction pendulum systems(FPS) is developed in this study. A fuzzy logic controller (FLC) is used to modulate the M damper force because the FLC has an inherent robustness and ability to handle non-linearities and uncertainties. A genetic algorithm (GA) is used for optimization of the FLC. When earthquake excitations are applied to the structures equipped with smart base isolation system, the relative displacement at the isolation level as well as the acceleration of the structure should be regulated under appropriate level. Thus, NSGA-II(Non-dominated Sorting Genetic Algorithm) is employed in this study as a multi-objective genetic algorithm to meet more than two control objectives, simultaneously. NSGA-II is used to determine appropriate fuzzy control rules as well to adjust parameters of the membership functions. Effectiveness of the proposed method for optimal design of the FLC is judged based on computed responses to several historical earthquakes. It has been shown that the proposed method can efficiently find Pareto optimal sets that can reduce both structural acceleration and base drift from numerical studies.

• Journal of Electrical Engineering and Technology
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• v.6 no.1
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• pp.133-138
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• 2011

This paper proposes a simple but robust self-tuning fuzzy logic controller for the speed regulation of a dual star induction machine based on indirect field oriented control. For feed the two star of this machine, two voltage source inverters based on sinus-triangular pulse-width modulation techniques are introduced. The simulation results show the robustness and good performance of the proposed controller.

• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.567-569
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• 2001

This paper presents improved performance on the speed control of Switched Reluctance Motor(SRM) by using fuzzy logic speed controller. The nonlinear model of SRM is used and the motor used in experiment is a 6/4 SRM. In order to prove the superiority of the fuzzy logic controller, it is applied to make use of Matlab simulation program. And to implement the control method on the SRM drive. DSP(TMS320F240) based SRM speed controller is designed and fabricated. The simulation and experiment results show that FLC is effective in settling time maximum overshoot and torque ripple.