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

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.

2009 Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control ◽

A new adaptive fuzzy controller for DC motor position control

10.1109/icsccw.2009.5379475 ◽

2009 ◽

Cited By ~ 1

Author(s):

Hadis Karimipour ◽

Heydar Toosian shandiz

Keyword(s):

Position Control ◽

Fuzzy Controller ◽

Dc Motor ◽

IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028) ◽

Enhancement of power system stability with FACTS using adaptive fuzzy controller

10.1109/icsmc.1999.816608 ◽

2003 ◽

Cited By ~ 2

Author(s):

T. Senjyu ◽

S. Yamane ◽

K. Uezato

Keyword(s):

Power System ◽

Fuzzy Controller ◽

Power System Stability ◽

System Stability ◽

The adaptive fuzzy designed PID controller using wavelet network

Computer Science and Information Systems ◽

10.2298/csis0902141e ◽

2009 ◽

Vol 6 (2) ◽

pp. 141-163 ◽

Cited By ~ 3

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 ◽

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).

PERFORMANCE IMPROVEMENTS OF SEPARATELY EXCITED DC MOTOR USING FUZZY LOGIC CONTROL

International Journal of Engineering Technologies and Management Research ◽

10.29121/ijetmr.v6.i2.2019.355 ◽

2020 ◽

Vol 6 (2) ◽

pp. 47-58

Author(s):

R. Nagarajan ◽

M. Gokulkannan ◽

T. Dinesh ◽

S. Murugesan ◽

M. Naveenprasanth

Keyword(s):

Fuzzy Logic ◽

Fuzzy Logic Controller ◽

Fuzzy Controller ◽

Expert Knowledge ◽

Speed Control ◽

Dc Motor ◽

Fuzzy Rules ◽

Motor Speed ◽

Performance Improvements ◽

Speed Performance

This paper demonstrates the importance of a fuzzy logic controller over conventional method. The performance of the separately excited DC motor is analyzed by using fuzzy logic controller (FLC) in MATLAB/SIMULINK environment. The FLC speed controller is designed based on the expert knowledge of the fuzzy rules system. The proposed DC motor speed control fuzzy rules are designed for fuzzy logic controller. The output response of the system is obtained by using fuzzy logic controller. The designed fuzzy controller for speed control performance is investigated. Significantly reducing the overshoot and shortening the settling time of the speed response of the motor. They validate different control of approaches, the simulation results show improvement in motor efficiency and speed performance.

IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222) ◽

A modified PI control action with a robust adaptive fuzzy controller applied to DC motor

10.1109/ijcnn.2001.939071 ◽

2002 ◽

Cited By ~ 7

Author(s):

N.B. Almutairi ◽

M.-Y. Chow

Keyword(s):

Fuzzy Controller ◽

Dc Motor ◽

Control Action ◽

Pi Control ◽

Adaptive Fuzzy ◽

Robust Adaptive

Direct adaptive fuzzy controller with small number of fuzzy rules for nanaffine nonlinear system

The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03. ◽

10.1109/fuzz.2003.1206638 ◽

2004 ◽

Cited By ~ 2

Author(s):

Jang-Hyun Park ◽

Seong-Hwan Kim ◽

Dong-Won Kim ◽

Gwi-Tae Park

Keyword(s):

Nonlinear System ◽

Fuzzy Controller ◽

Fuzzy Rules ◽

Embedded two level direct adaptive fuzzy controller for DC motor speed control

Ain Shams Engineering Journal ◽

10.1016/j.asej.2015.10.003 ◽

2018 ◽

Vol 9 (1) ◽

pp. 65-75 ◽

Cited By ~ 11

Author(s):

Ahmad M. Zaki ◽

Mohammad El-Bardini ◽

F.A.S. Soliman ◽

Mohammed Mabrouk Sharaf

Keyword(s):

Fuzzy Controller ◽

Speed Control ◽

Dc Motor ◽

Motor Speed ◽

Fuzzy Adaptive Controller Design for IPMSM with System Uncertainties and Disturbances Considering

Journal of Science and Technology - Technical Universities ◽

10.51316/30.8.4 ◽

2020 ◽

Vol 30.8 (147) ◽

pp. 22-26

Author(s):

Nga Thi-Thuy Vu ◽

Keyword(s):

Fuzzy Controller ◽

Controller Design ◽

Sudden Change ◽

Fuzzy Model ◽

Lyapunov Theory ◽

System Stability ◽

Nonlinear Dynamic Model ◽

Adaptive Fuzzy ◽

System Uncertainties

This paper proposed a T-S fuzzy model based adaptive fuzzy controller for the interior permanent magnet synchronous motors. Firstly, the T-S fuzzy model of the system is built based on the nonlinear dynamic model. Next, the adaptive fuzzy controller is designed to deal with the problems of system uncertainties and external disturbances. This controller includes two phases, one is for system stability and one for compensating the effect of the unknown components. The stability of the system, as well as the convergence of the adaptive law, is mathematically proven through Lyapunov theory. Finally, some simulations are done to verify the effectiveness of the presented scheme. The simulation results show that the proposed algorithm has a good response to the change of reference input, the system parameters variation, and the sudden change of the load torque.