직류전압 퍼지 제어 기반의 3상 Z-소스 PWM 정류기

<p>This paper directed the speed-sensorless vector control of induction motor drive with PI and fuzzy controllers. Natural observer with fourth order state space model is employed to estimate the speed and rotor fluxes of the induction motor. The formation of the natural observer is similar to and as well as its attribute is identical to the induction motor. Load torque adaptation is provided to estimate the torque and rotor speed is estimated from the load torque, rotor fluxes and stator currents. There is no direct feedback in natural observer and also observer gain matrix is absent. Both the induction motor and the observer are characterized by state space model. Simple fuzzy logic controller and conventional PI controllers are used to control the speed of the induction motor in closed loop. MATLAB simulations are made with PI and fuzzy controllers and the performance of fuzzy controller is better than PI controller in view of torque ripples. The simulation results are obtained for various running conditions to exhibit the suitability of this method for sensorless vector control. Experimental results are provided for natual observer based sensorless vector control with conventional PI controller.</p>

In this paper mathematical models of Mamdani type fuzzy PI or PD controllers via Center of Gravity (CoG) defuzzification are obtained. These controllers are called the simplest fuzzy PI or PD controllers as minimal number of fuzzy sets (two fuzzy sets on each of the two-input variables and three fuzzy sets on the output variable) are employed. The basic components of these models are L-type and Y-type membership functions on each input variable, trapezoidal or triangular membership functions on output variable, minimum / algebraic product t-norm, maximum OR operator, three linear fuzzy control rules, minimum / algebraic product inference and CoG defuzzification. Mathematical models of fuzzy controller via CoG defuzzification were not reported in the literature and are therefore presented for the first time in this paper.

A fuzzy logic-based control system in household split-type air conditioners (AC) offers an alternative approach to reducing excess energy consumption without compromising thermal comfort. This study aims to test the effectiveness of three types of membership functions (MF), namely triangular, trapezoidal, and Gaussian, in improving energy efficiency and the stability of room temperature and humidity control. Simulations were performed using MATLAB software with the Mamdani fuzzy inference method and centroid defuzzification technique. The three MF were tested using 30 sets of temperature and humidity data to analyze their effect on fan speed and power consumption. The simulation results show that the trapezoidal MF provides the highest energy efficiency of 57.24%, followed by the Gaussian MF at 56.80% and the triangular MF at 53.71%. These findings indicate that fuzzy systems can significantly reduce energy consumption compared to conventional air conditioner controllers. This research is expected to serve as a reference in the development of more energy-efficient intelligent control systems.

This study investigates the analytical structure of a two-input two-output fuzzy controller which employs triangular-shaped input fuzzy sets, trapezoidal-shaped output fuzzy sets, linear control rules, probabilistic AND fuzzy logic, Lukasiewcz OR fuzzy logic, Mamdani's minimum inference method and a center of gravity defuzzification algorithm. The structure of the fuzzy controller is proven to be the sum of global 4D multilevel relays and local nonlinear proportional-integral (PI) controllers with variable gains continuously changing with process outputs. The global multilevel relays play a major role in determining control action of the fuzzy controller while the local PI controllers locally fine tune the control action of the relays. As the number of control rules approaches /spl infin/, each global 4D multilevel relay becomes the sum of two global linear PI controllers while the local PI controllers disappear. >

This paper presents a systematic design method of a controller using the discrete fuzzy PI control and the particle swarm optimization (PSO). Unlike a conventional PI controller, the discrete fuzzy PI controller has variable gains according to its input variables. Generally, it is complicated to tune the parameters of a fuzzy controller because there are too many parameters which are strongly coupled. In the discrete-time domain, the discrete fuzzy PI controller is a superset of the conventional PI controller. And the initial parameters of the fuzzy PI controller are selected by using the inclusion relationship. And, for the sake of simplicity, only four rules are used to construct a nonlinear fuzzy control surface. The tuning parameters of the discrete fuzzy PI controller are optimized by using the PSO. To verify the effectiveness of the controller designed by using the discrete fuzzy PI control and the PSO, we applied it to a wind turbine pitch controller. As a result, the proposed controller has variable gains, unlike the PI controller, and make the pitch controller operate in boarder operating regions.

Power conditioning system (PCS) is an interface between distributed generation and utility grid. It regulates voltage, current and power transmitted from the hybrid direct fuel-cell/turbine (DFC/T) power plant to utility grid. This paper presents a self-adaptive fuzzy PI controller of three phase inverter in PCS for the DFC/T power plant. One of the main tasks of PCS for distributed generations is power flow control. Traditionally, the control scheme for grid connected inverters is PI controller. However, PI control scheme would lead to large overshoot and long response time when the load increases sharply. Thus, a hybrid fuzzy PI control scheme is proposed in this paper in order to improve the power flow control. The fuzzy controllers can adjust the PI parameters according to the voltage error and the derivative of voltage error. The overall self-adaptive tuning fuzzy PI controller can update the PI parameters in real time, which makes the power flow control much more accurate than conventional PI controllers. Analysis and design methodologies of hybrid fuzzy PI controllers are presented in this paper. The model was developed in Matlab/Simulink environment. Simulation results show that the proposed control scheme can effectively reduce the overshoot and response time compared to the conventional PI controllers.

Recently, crime prevention is one of the overall strategies to reduce crime around the world including Malaysia. This is in lines with one of the Malaysian government agendas as stated in National Key Result Area (NKRA) which is to reduce crime. Because of the decision making process for crime prevention is manually done by the police, so it is difficult to determine the level of crime based on crime volume. That requires a method of selection to help the police to determine which is the most appropriate decision based on the level of crime. Therefore, this research developed prototype of decision support system (DSS) by using Mamdani-type fuzzy inference system (FIS) to classify the crime level and to improve the process of decision making for crime prevention. But, it is well-known that selection of membership functions (MF) is the key problem in the design of a FIS. Therefore, this paper outlines the basic comparisons between the triangular MF and trapezoidal MF. The experimental results of the two MFs are compared. It also shows which one is a better choice of the two MFs used in FIS in order to improve the efficiency of decision making for crime prevention. The result of the study indicates that triangular MF gives better classification of crime level but trapezoidal MF gives result closer to triangular MF.

Over the past two decades, there has been a rapidly growing interest in approximating a nonlinear system by a Takagi-Sugeno (T-S) fuzzy model (Takagi & Sugeno, 1985). In general, this model is represented by using a set of fuzzy rules to describe a global nonlinear system in terms of a set of local linear models which are smoothly connected by fuzzy membership functions. Based on the T-S fuzzy model, recently, various fuzzy controllers have been developed under the so-called parallel-distributed compensation (PDC) scheme (in which each control rule is distributively designed for the corresponding rule of a T-S fuzzy model) and have been widely and successfully applied in fields ranging from aerospace to process control. The reason is because the fuzzy model-based control method provides a natural, simple and effective design approach to complement other nonlinear control techniques that require special and rather involved knowledge. Below are listed the main features of T-S model-based fuzzy control method: 1. It does not require severe structural assumptions on the plant model. 2. It preserves well-understood linear intuition. 3. It is naturally compatible with decompositions of the overall control problem. The decompositions are typically not hierarchical, and the interactions of sub-problems are captured by physical variables that are typically state variables in a more complete model of the overall system. 4. It enables control systems to respond rapidly to changing operating conditions. For this reason, it is important that the selected physical variables reflect changes in plant dynamics as operating conditions change. In fact, the T-S model-based fuzzy control method (of divide and conquer type) constructs a nonlinear controller, with certain required dynamic properties, by combining, in some sense, the members of appropriate family of linear time-invariant controllers. Here, nonlinear control design task is broken into a number of linear sub-problems, which enable linear design methods to be applied to nonlinear problems. Within the general framework of the T-S fuzzy model-based control method, a flurry of research activities have quickly yielded many important results on the design of fuzzy control systems by means of the following Lyapunov function approaches: 1. Common quadratic Lyapunov function approach (Tanaka & Sugeno, 1992; Tanaka et al, 1996; Wang et al, 1996; Cao & Frank, 2000; Assawinchaichote, 2004).

PMSM servo system has nonlinear, parameter time-varying, load disturbance and other issues. In the face of these problems, traditional PI control strategies often fail to achieve ideal parameter tracking and load immunity, showing a large degree of limitations. Aiming at this kind of problem, this paper designs a fuzzy PI control method combining PI control and fuzzy control, and uses the vector rotation control method of current speed double closed loop to drive the permanent magnet synchronous motor for control. Finally, the traditional PI control system and fuzzy PI control system simulation model are built in Matlab/Simulink environment. The simulations were carried out in three cases of high speed, medium speed and low speed, and the speed and current waveforms of the two control methods were observed. The simulation results show that the fuzzy PI control system can increase the response rate, reduce the overshoot of the speed and reduce the current fluctuation in the full speed range. The robustness of the system is improved, the adaptability of the system to load changes is enhanced, and the correctness of the theoretical analysis is verified.

The nuclear steam generator is a nonminimum-phase system, which is caused by the swell and shrink effects. Since its inverse system has unstable dynamics, it is difficult to train the fuzzy controller via the conventional backpropagation of the system output errors. In this paper, a genetic algorithm is applied for the simultaneous design of membership functions and rule sets for a fuzzy control method for a steam generator water level. The genetic fuzzy controller for the steam generator is a fuzzy logic controller which is tuned offline by the genetic algorithm using the water level, feedwater flowrate, and steam flowrate signals of the steam generator. The symmetric Gaussian membership functions based on the flowrate and water level errors are applied. The proposed genetic fuzzy controller has a generalized and simplified rule base. The same genetic algorithm that is used to optimize the genetic fuzzy controller tunes a conventional proportional-integral (P-I) controller, and the performance of two controllers is compared. The genetic fuzzy controller shows good response that its swell and shrink phenomena are smaller and its response is faster than those of a well-tuned P-I controller are.

An efficient intelligent system (FUZZY LOGIC) for the power transformer residual life assessment is presented in this paper. In the fuzzy method, different inputs and one output is used, that strictly depends on the number of membership function and their rule base and the type of the defuzzification method. The residual life of the transformers greatly influenced by the condition of the transformer insulation system. The insulation of winding de-teriorates during the normal ageing of the transformers. Paper insulation that contains cellulose molecules degrades due to heating or electrical breakdown dissolved in the transformer oil. Hence, the analysis of the Transformer oil gives correct evidence of variation in the property of the winding insulation during operation. Deterioration in transformer oil-paper insulation decreases both its electrical and mechanical strength that influence the age of the transformer. This paper presents a fuzzy logic method that has been implemented on three samples of power transformer oil for the residual life assessment of the transformers.

In industry or any area increasing load is a vast problem for power generation plants due to increase in demand for power. So making balance between generation and demand is the operating principle of load frequency control (LFC). The reliable operation of a large interconnected power system necessarily requires an Automatic Generation Control (AGC). The objective of AGC is to regulate the power output of Generators within a specified area in response to change in the system frequency, tie line power or relation of the two to each other, so as to maintain the scheduled system frequency and power interchange in the other are within the prescribed limits. This paper presents the use of conventional PI controller and artificial intelligence to study the load frequency control of interconnected power system. In the proposed scheme, a control methodology is developed using conventional PI controller and Fuzzy Logic controller (FLC) for interconnected hydro-thermal power system. The control strategies guarantees that the steady state error of frequencies and inadvertent interchange of tie-lines power are maintained in a given tolerance limitations. The performances of the controllers are simulated using MATLAB/SIMULINK package. A comparison of Fuzzy controller and PI controller based approaches shows the superiority of proposed Fuzzy logic controller for step change in loading conditions. The simulation results also tabulated as a comparative performance in view of settling time and peak over shoot.

Welding operation decides the quality of product standards in all metal work products like automobiles, aerospace vehicles, and many more. The quality of the welding process is more reliable by automating the process with robots. In this research work, the GMAW operation is automated with the ‘Fanuc Robot Arc mate 100iC/12’ robot. The material characteristics such as ultimate tensile strength, hardness, and impact strength of weldments are predicted using a fuzzy system using triangular membership function (TrMF) and trapezoidal membership function (TMF). The simulated results are validated by comparing with experimental work, the experiments are designed using orthogonal array L18, and material characteristics are studied using fractography test. The fuzzy system is trained with experimental results using the IF-Then rule base with the help of the L18 orthogonal array. The inference system has predicted the accuracy rate of weldment mechanical properties, showing a lower error rate.

Research on adaptive deformable mirror technology for voice coil actuators is an important trend in the development of large ground-based telescopes. A voice coil adaptive deformable mirror contains a large number of actuators, and there are problems with structural coupling and large temperature increases in their internal coils. Additionally, parameters of the traditional proportional integral derivative control cannot be adjusted in real-time to adapt to system changes. These problems can be addressed by introducing fuzzy control methods. A table lookup method is adopted to replace real-time calculations of the regular fuzzy controller during the control process, and a prototype platform has been established to verify the effectiveness and robustness of this process. Experimental tests compare the control performance of traditional and fuzzy proportional integral derivative controllers, showing that, in system step response tests, the fuzzy control system reduces rise time by 20.25%, decreases overshoot by 78.24%, and shortens settling time by 67.59%. In disturbance rejection experiments, fuzzy control achieves a 46.09% reduction in the maximum deviation, indicating stronger robustness. The fuzzy proportional integral derivative controller, based on table lookup, outperforms the standard controller significantly, showing excellent potential for enhancing the dynamic performance and disturbance rejection capability of the voice coil motor actuator system.

Alternative current motors are generally modelled with constant system parameters. However, parameters of the motor show variations during operation due to complex, unidentified and nonlinear system dynamics. Fuzzy logic controllers are widely used as a solution to overcome this problem. Because, fuzzy logic controllers don’t be affected from model uncertainties of the system to be controlled. In this study, permanent magnet synchronous motor that is one of the alternative current motors is controlled with fuzzy logic controller. All stages of the designed controllers are developed by using unique softwares instead of ready toolboxes. Triangular and trapezoidal membership functions with Mamdani and Larsen inference methods are used in the designed fuzzy logic controllers. Speed control performances of the fuzzy logic controllers have been investigated and compared with each other in the simulation studies. According to the obtained results from simulation studies, it is observed that fuzzy logic controllers designed with Larsen fuzzy inference method and trapezoidal input membership function provides better performance.