Gain Tuning of a Fuzzy Logic Controller Superior to PD Controllers in Motor Position Control

Young-Real Kim 한국지능시스템학회 2014 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.14 No.3

Although the fuzzy logic controller is superior to the proportional integral derivative (PID) controller in motor control, the gain tuning of the fuzzy logic controller is more complicated than that of the PID controller. Using mathematical analysis of the proportional derivative (PD) and fuzzy logic controller, this study proposed a design method of a fuzzy logic controller that has the same characteristics as the PD controller in the beginning. Then a design method of a fuzzy logic controller was proposed that has superior performance to the PD controller. This fuzzy logic controller was designed by changing the envelope of the input of the of the fuzzy logic controller to nonlinear, because the fuzzy logic controller has more degree of freedom to select the control gain than the PD controller. By designing the fuzzy logic controller using the proposed method, it simplified the design of fuzzy logic controller, and it simplified the comparison of these two controllers.

Gain Tuning of a Fuzzy Logic Controller Superior to PD Controllers in Motor Position Control

Kim, Young-Real Korean Institute of Intelligent Systems 2014 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.14 No.3

Although the fuzzy logic controller is superior to the proportional integral derivative (PID) controller in motor control, the gain tuning of the fuzzy logic controller is more complicated than that of the PID controller. Using mathematical analysis of the proportional derivative (PD) and fuzzy logic controller, this study proposed a design method of a fuzzy logic controller that has the same characteristics as the PD controller in the beginning. Then a design method of a fuzzy logic controller was proposed that has superior performance to the PD controller. This fuzzy logic controller was designed by changing the envelope of the input of the of the fuzzy logic controller to nonlinear, because the fuzzy logic controller has more degree of freedom to select the control gain than the PD controller. By designing the fuzzy logic controller using the proposed method, it simplified the design of fuzzy logic controller, and it simplified the comparison of these two controllers.

Gain Tuning of a Fuzzy Logic Controller Superior to PD Controllers in Motor Position Control

김영렬 한국지능시스템학회 2014 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.14 No.3

Although the fuzzy logic controller is superior to the proportional integral derivative (PID)controller in motor control, the gain tuning of the fuzzy logic controller is more complicatedthan that of the PID controller. Using mathematical analysis of the proportional derivative (PD)and fuzzy logic controller, this study proposed a design method of a fuzzy logic controller thathas the same characteristics as the PD controller in the beginning. Then a design method of afuzzy logic controller was proposed that has superior performance to the PD controller. Thisfuzzy logic controller was designed by changing the envelope of the input of the of the fuzzylogic controller to nonlinear, because the fuzzy logic controller has more degree of freedom toselect the control gain than the PD controller. By designing the fuzzy logic controller usingthe proposed method, it simplified the design of fuzzy logic controller, and it simplified thecomparison of these two controllers.

Intelligent Prevent the Risk of Carcinoma of the Lung Progression

Sareh Mohammadi Jaberi,Farzin Piltan,Amirzubir Sahamijoo,Nasri b Sulaiman 보안공학연구지원센터 2015 International Journal of Bio-Science and Bio-Techn Vol.7 No.4

Smog hanging over cities is the most familiar and obvious form of air pollution. The effects of inhaling particulate matter have been studied in humans and animals and include asthma, lung cancer, cardiovascular issues, and premature death. There are, however, some additional products of the combustion process that include nitrogen oxides and sulfur and some un-combusted hydrocarbons, depending on the operating conditions and the fuel-air ratio. Tuning the fuel to air ratio caused to control the lung cancer. Lung cancers are tumors arising from cells lining the airways of the respiratory system. Design of a robust nonlinear controller for automotive engine can be a challenging work. This research paper focuses on the design and analysis of a high performance PID like fuzzy controller for automotive engine, in certain and uncertain condition. The proposed approach effectively combines of design methods from linear Proportional-Integral-Derivative (PID) controller and fuzzy logic theory to improve the performance, stability and robustness of the automotive engine. To solve system’s dynamic nonlinearity, the PID fuzzy logic controller is used as a PID like fuzzy logic controller. The PID like fuzzy logic controller is updated based on gain updating factor. In this methodology, fuzzy logic controller is used to estimate the dynamic uncertainties. In this methodology, PID like fuzzy logic controller is evaluated. PID like fuzzy logic controller has three inputs, Proportional (P), Derivative (D), and Integrator (I), if each inputs have linguistic variables to defined the dynamic behavior, it has ×× linguistic variables. To solve this challenge, parallel structure of a PD-like fuzzy controller and PI-like fuzzy controller is evaluated. In the next step, the challenge of design PI and PD fuzzy rule tables are supposed to be solved. To solve this challenge PID like fuzzy controller is replaced by PD-like fuzzy controller with the integral term in output. This method is caused to design only PD type rule table for PD like fuzzy controller and PI like fuzzy controller.

직류 전동기 속도 제어에서 PI 제어기보다 우수한 퍼지 논리 제어기의 이득 선정을 위한 연구

김영렬(Young-Real Kim) 한국조명·전기설비학회 2014 조명·전기설비학회논문지 Vol.28 No.6

Through a lot of papers, it has been concluded that fuzzy logic controller is superior to PI controller in motor speed control. Although fuzzy logic controller is superior to PI controller in motor speed control, the gain tuning of fuzzy logic controller is more complicated than that of PI controller. In this paper, using mathematical analysis of the PI and fuzzy controller, the design method of the fuzzy controller that has the same characteristics with the PI controller is proposed. After that, we can design the fuzzy controller that has superior performance than PI controller by changing the envelope of input of fuzzy controller to nonlinear, because the fuzzy controller has more degree of freedom to select the control gain than PI controller. The advantage of fuzzy logic controller is shown through mathematical analysis, and the simulation result using Matlab simulink has been proposed to show the effectiveness of these analysis.

Designing of Fuzzy Logic Controller for Liquid Level Controlling

Ashish Singh Thakur,Himmat Singh,Sulochana Wadhwani 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.6

In control system there are a number of general systems and methods which are encountered in all areas of industry and technology. There are many ways to control any system, in which fuzzy is often the very best way. The only reason is faster and cheaper. One of successful application that is used for the controlling of liquid level is fuzzy logic controller. In order to find the best design to stabilize the liquid level in this method, some factors will be considered. For this paper, the liquid level was controlled by using three rules of membership function which then extended to five rules, seven rules and nine rules for verification purpose and further improvement of the system. This paper was focused to the software part only. By doing some modification in this paper, the design will be very useful for the system relates to liquid level control that widely use in industry nowadays. For a long time, the selection and definition of the parameters of PID controller are very difficult. There must be a bad effect if you do not choose nicely parameters. To strictly limit the overshoot, the use of Fuzzy controller can achieve a great control cause. In this paper, we take the liquid level water tank, and use MATLAB to design a Fuzzy Controller. Then we analyze the control effect and compare it with the effect of PID controller. As a result of comparing, Fuzzy Logic Controller is superior to PID controller. Comparison of the control results from these two systems indicated that the Fuzzy logic controller significantly reduced overshoot and steady state error.

Performances Evaluation and Comparison of PID Controller and Fuzzy Logic Controller for Process Liquid Level Control

Deepa Shivshant Bhandare,N. R.Kulkarni 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10

Tank level control systems can be found everywhere. It is essential for control systems engineers to understand how tank control system work and how the level control problems are solved. In industrial control systems the liquid level is carrying its significance as the control action for level control in tanks containing different chemicals or mixtures is essential for further control linking set points. All the real systems exhibits non-linear nature, conventional controllers are not always able to provide good and accurate results. Fuzzy Logic Control is used to obtain better response. Fuzzy Logic is a paradigm for an alternative design methodology, which can be applied in developing both linear and non-linear systems for embedded control. By using fuzzy logic, designers can realize lower development costs, superior features, and better end product performance. One of the successful applications that used fuzzy control is tank liquid level control. In this paper, we take the liquid level water tank, and use MATLAB to design a Fuzzy Control. Then we analyze the control effect and compare it with the effect of PID controller. As a result of comparison, Fuzzy Control is superior to PID control. Especially it can give more attention to various parameters, such as the time of response, the error in steady state and overshoot. Comparison of the resultant control response from these two systems indicated that the fuzzy logic controller significantly reduced overshoot and steady state error.

Optimization of Type-2 Fuzzy Logic Controllers for a Non-Rigid Airship Using Clonal Selection Algorithm

Christine Barrera,Ju-Jang Lee 제어로봇시스템학회 2011 제어로봇시스템학회 각 지부별 자료집 Vol.2011 No.12

In the past, non-rigid airship/blimp control was commonly implemented using standard fuzzy logic controllers also known as type-1 fuzzy logic controllers. For better performance, the membership function of the type-1 fuzzy controller was further optimized using different optimization methods with Genetic Algorithm (GA) being the most common. These days, however, a lot of attention is being focused on type-2 fuzzy logic controllers due to its better performance on environments with uncertainties. New optimization methods for the membership function were also being discovered. Among these methods is the Clonal Selection Algorithm (CLONALG) which was inspired from clonal selection principle based on the basic features of an adaptive immune response to an antigenic stimulus. It was proven to have superior performance than Genetic Algorithm because of its faster convergence speed and better fitness values. This method, however, still needs to be tested on an actual control application. This project developed both type-l and type-2 fuzzy logic controllers optimized by using both GA and CLONALG for a blimp control problem. The control system was divided into three parts: velocity, heading and elevation. This research provides a comparison and validates the performance benefits of type-2 over type-1 fuzzy control and CLONALG over GA. A better control with shorter rise time and settling time, less error and less sensitivity to uncertainties was also achieved by using type-2 fuzzy control combined with CLONALG optimization.

Fuzzy logic based improved Active and Reactive Power control operation of DFIG for Wind Power Generation

J.P. Mishra,Debirupa Hore,Asadur Rahman 전력전자학회 2011 ICPE(ISPE)논문집 Vol.2011 No.5

The fuzzy-controllers are designed to tune along with the conventional PI-controllers for the vector control of active and reactive power of a wind-turbine driven DFIG under varying wind speed operation to optimize the power generation at specified power-factor. Initially, stator-flux-oriented vector control scheme is implemented using tuned active and reactive power PI-controllers for the rotor-side-converter. Then the fuzzy-controllers are also tuned along with conventional PI-controllers for the generated active power to track more precisely the reference power at specified power-factor in both sub-synchronous and super-synchronous modes of operations. The grid-side-converter is controlled in grid-voltage-oriented reference frame using dc-link voltage PI-controller. Hysteresis current controlled based PWM switching of both rotor-side and grid-side converters ensure fast and accurate control of active and reactive power. Simulation results under varying wind conditions reveal that the additional fuzzy-controller improves the performance of variable speed wind power generating system using DFIG.

Design of Optimal Fuzzy Logic based PI Controller using Multiple Tabu Search Algorithm for Load Frequency Control

Saravuth Pothiya,Issarachai Ngamroo,Suwan Runggeratigul,Prinya Tantaswadi 대한전기학회 2006 International Journal of Control, Automation, and Vol.4 No.2

This paper focuses on a new optimization technique of a fuzzy logic based proportional integral (FLPI) load frequency controller by the multiple tabu search (MTS) algorithm. Conventionally, the membership functions and control rules of fuzzy logic control are obtained by trial and etTor method or experiences of designers. To overcome this problem, the MTS algorithm is proposed to simultaneously tune proportional integral gains, the membership functions and control rules of a FLPT load frequency controller in order to minimize the frequency deviations of the interconnected power system against load disturbances. The MTS algorithm introduces additional techniques for improvement of the search process such as initialization, adaptive search, multiple searches, crossover and restart process. Simulation results explicitly show that the performance of the proposed FLPI controller is superior to conventional PI and FLPI controllers in terms of overshoot and settling time. Furthermore, the robustness of the proposed FLPI controller under variation of system parameters and load change are higher than that of conventional PI and FLPI controllers.