scholarly journals The adaptive fuzzy designed PID controller using wavelet network

2009 ◽  
Vol 6 (2) ◽  
pp. 141-163 ◽  
Author(s):  
Emary El ◽  
Walid Emar ◽  
Musbah Aqel
Keyword(s):  
Fuzzy Logic ◽  
Adaptive Control ◽  
Pid Controller ◽  
Fuzzy Controller ◽  
Expert Knowledge ◽  
Wavelet Network ◽  
Fuzzy Adaptive Control ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy ◽  
Fuzzy Adaptive

During the past several years, fuzzy control has emerged as one of the most active and fruitful areas for research in the applications of the fuzzy set theory, especially in the realm of the industrial processes, which do not lend themselves to control by conventional methods because of a lack of quantitative data regarding the inputoutput relations i.e., accurate mathematical models. The fuzzy logic controller based on wavelet network provides a means of converting a linguistic control strategy based on expert knowledge into an automatic strategy. In the available literature, one can find scores of papers on fuzzy logic based controllers or fuzzy adaptation of PID controllers. However, relatively less number of papers is found on fuzzy adaptive control, which is not surprising since fuzzy adaptive control is relatively new tool in control engineering. In this paper, fuzzy adaptive PID controller with wavelet network is discussed in subsequent sections with simulations. An adaptive neural network structure was proposed. This structure was used to replace the linearization feedback of a second order system (plant, process). Also, in this paper, it is proposed that the controller be tuned using Adaptive fuzzy controller where Adaptive fuzzy controller is a stochastic global search method that emulates the process of natural evolution. It is shown that Adaptive fuzzy controller be capable of locating high performance areas in complex domains without experiencing the difficulties associated with high dimensionality or false optima as may occur with gradient decent techniques. From the output results, it was shown that Adaptive fuzzy controller gave fast convergence for the nonparametric function under consideration in comparison with conventional Neural Wavelet Network (NWN).

Adaptive Fuzzy Control of Tension Variations due to the Eccentric Unwinding Roll in Multi-Span Web Transport Systems

1999 ◽  
Author(s):  
Sungchul Jee ◽  
Sungchol Kim ◽  
Kee-Hyun Shin
Keyword(s):  
Real Time ◽  
Transport System ◽  
Pid Controller ◽  
Fuzzy Controller ◽  
Adaptive Fuzzy Control ◽  
Transport Systems ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy ◽  
Ac Motors ◽  
The Web

Abstract Disturbances due to the eccentric unwinding roll/roller can create significant tension variations in the web spans in multi-driven web transport systems. The drivers are interconnected by an elastic web material (a 14-micrometer thick Polypropylene film was used for this study). Specifically, eccentricity of an upstream roller such as an unwinding roll creates upstream tension variation, and the tension variation in upstream web spans is transported to downstream due to the interconnection between the web spans. However, it is not easy to obtain an accurate mathematical model of the web transport system, for the design of a tension controller, which contains several actuators, sensors, gears, bearing systems, and interconnected web spans. In this paper, an adaptive fuzzy controller which automatically tunes its parameters in real-time is suggested to regulate tension variations in multi-spans due to an eccentric unwinding roll. In the proposed method, the input and output ranges are adjusted together according to continuous observation of tension error and change in the error. The proposed controller is implemented on a prototype web transport system which consists of three AC motors, real-time target controllers, unwinding and winding rolls with two-spans of web including sensors for the measurement of tensions and speeds of the web. The Experimental results show that the proposed adaptive fuzzy controller successfully rejects the effects of the disturbances due to the eccentric unwinding roll and substantially reduces the tension variations in both web spans. The adaptive fuzzy controller outperformed the conventional PID controller as the frequency of the eccentricity disturbance increased.

scholarly journals Speed Control of Seperately Excited DC motor for Electric Vehicles (EVs) By Inverse Model Based Fuzzy Learning Controller

2021 ◽  
Author(s):  
mehmet bulut
Keyword(s):  
Fuzzy Controller ◽  
Expert Knowledge ◽  
Dc Motor ◽  
Inverse Model ◽  
Fuzzy Rules ◽  
System Stability ◽  
Model Learning ◽  
Adaptation Mechanism ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy

The adaptation mechanism, which adjusts the controller coefficients according to the parameter changes in the system, ensures that the controller is adaptable. Fuzzy logic can be used to calculate the gain coefficients of the controller in the system by using the adaptive fuzzy method instead of a traditional algorithm for the adaptation mechanism. Normally, the rules of a fuzzy controller system are derived from the system's internal structure and system behavior using expert knowledge that has experienced the system. However, it is not possible to derive fuzzy rules based on expert human knowledge for all systems in this way. It is necessary to use different methods to derive fuzzy rules in highly variable behavior and nonlinear systems. In this study, an adaptive fuzzy controller design for dc motor was made using a learning-based reference model learning algorithm using fuzzy inverse model; It has been shown that it is applicable for dc motors with the results obtained. Simulation of the designed system was carried out using the Matlab program, and the behavior of the system was investigated by using constant and variable loads. The results showed that it is satisfactory to drive a dc motor with adaptive fuzzy controller in terms of system stability.

High-efficiency gear hobbing technics based on fuzzy adaptive control of spindle torque

2018 ◽  
Vol 233 (10) ◽  
pp. 3331-3345 ◽  
Author(s):  
Xing Liu ◽  
Fei Zhao ◽  
Xuesong Mei ◽  
Tao Tao ◽  
Jianguang Shen
Keyword(s):  
Adaptive Control ◽  
High Efficiency ◽  
Control Method ◽  
Fuzzy Controller ◽  
Numerical Control ◽  
Feedback Signal ◽  
Fuzzy Adaptive Control ◽  
Gear Hobbing ◽  
The Relationship ◽  
Fuzzy Adaptive

In this paper, the problem of relatively low efficiency of the current gear hobbing process is addressed through fuzzy adaptive control of the cutting force. The paper studies the influencing factors of the cutting torque of gear hobbing, and the relationship between the feed rate and the cutting torque is established. Based on the relationship and the fuzzy adaptive control method, a high efficiency gear hobbing method is designed. A methodology using the static spindle torque rather than the dynamic one as the feedback signal of the fuzzy controller is also presented, which can deal with the severe cutting torque fluctuations during gear hobbing. The input and the output scaling factors of the fuzzy controller can also be tuned online to adapt to different types of gears or various cutting conditions. The key issue of determining the reference value of the spindle torque is also resolved through analysis of the spindle torque data in a trial cut. The proposed method is simulated and implemented on a numerical control gear hobbing machine, which cuts spur gears and helical gears. The simulation and experimental results are in a good consistency. The efficiency is improved considerably, which saves as high as 40% and 30% cutting time of the gear hobbing process in the first and second set of experiments, respectively.

scholarly journals A Fuzzy Adaptive Control of Doubly Fed Induction Machine (DFIM)

2018 ◽  
Vol 208 ◽  
pp. 03008
Author(s):  
A. Saidi ◽  
Lamia Youb ◽  
Farid Naceri ◽  
Sebti Belkacem
Keyword(s):  
Adaptive Control ◽  
Fuzzy Control ◽  
Adaptive Fuzzy Control ◽  
Induction Machine ◽  
Fuzzy Adaptive Control ◽  
Adaptive Fuzzy ◽  
Doubly Fed Induction Machine ◽  
Doubly Fed ◽  
The Stability ◽  
Fuzzy Adaptive

In this paper, we are interested in the adaptive fuzzy control a technique has been studied and applied, namely adaptive fuzzy control based on theory of Lyapunov. The system based on the stability theory is used to approximate the gains Ke and kdce to ensure the stability of the control in real time .the simulations results obtained by using Matlab environment gives that the fuzzy adaptive control more robust, also it has superior dynamics performances. The results and test of robustness will be presented.

scholarly journals Speed Control of Seperately Excited DC motor for Electric Vehicles (EVs) By Inverse Model Based Fuzzy Learning Controller

2021 ◽  
Author(s):  
mehmet bulut
Keyword(s):  
Fuzzy Controller ◽  
Expert Knowledge ◽  
Dc Motor ◽  
Inverse Model ◽  
Fuzzy Rules ◽  
System Stability ◽  
Model Learning ◽  
Adaptation Mechanism ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy

The adaptation mechanism, which adjusts the controller coefficients according to the parameter changes in the system, ensures that the controller is adaptable. Fuzzy logic can be used to calculate the gain coefficients of the controller in the system by using the adaptive fuzzy method instead of a traditional algorithm for the adaptation mechanism. Normally, the rules of a fuzzy controller system are derived from the system's internal structure and system behavior using expert knowledge that has experienced the system. However, it is not possible to derive fuzzy rules based on expert human knowledge for all systems in this way. It is necessary to use different methods to derive fuzzy rules in highly variable behavior and nonlinear systems. In this study, an adaptive fuzzy controller design for dc motor was made using a learning-based reference model learning algorithm using fuzzy inverse model; It has been shown that it is applicable for dc motors with the results obtained. Simulation of the designed system was carried out using the Matlab program, and the behavior of the system was investigated by using constant and variable loads. The results showed that it is satisfactory to drive a dc motor with adaptive fuzzy controller in terms of system stability.

Adaptive fuzzy controller based MPPT for photovoltaic systems

2014 ◽  
Vol 78 ◽  
pp. 843-850 ◽  
Author(s):  
Ouahib Guenounou ◽  
Boutaib Dahhou ◽  
Ferhat Chabour
Keyword(s):  
Fuzzy Controller ◽  
Photovoltaic Systems ◽  
Adaptive Fuzzy Controller ◽  
Adaptive Fuzzy
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