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This study focused on the development of a system based on evolutionary Algorithms to obtain the optimum parameters of the fuzzy controller to increase the convergence speed and accuracy of the controller. The aim of the study is to design fuzzy controller without expert’s knowledge by using evolutionary genetic algorithms and carry out on a DC mot...
Contexts in source publication
Context 1
... this reason, the fuzzy systems includes a fuzzifier section for inputs and a defuzzifier section for outputs. The schematic structure of the commonly used fuzzy controller system consists of five parts as shown in Figure 1. The input parameters of the fuzzy controller are processed by the inference engine using a rule-based fuzzy set. ...
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... the termination criterion determined in the genetic algorithm is reached, the fuzzy-PI controller containing the best performing rule base according to the fitness function is applied to the derived transfer function model of the dc motor to be controlled. Figure 10. For 10 generations, the fuzzy-PI system responses of dc motor control a) without load for 1375 rpm, b) with load for 500 rpm, c) with load for 1000 rpm. ...
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... is seen that, system outputs of the DC Motor mathematical model gives ideal responses that do not have overshoots and oscillations. For inputs with different speed reference values the results of the system responses generated are shown in Figure 10(a) and Figure 10(b). The rule base obtained by using 10 generations in the learning process of the fuzzy rule base with the evolutionary algorithm does not represent a sufficiently defined fuzzy control to control the dc motor under load. ...
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... is seen that, system outputs of the DC Motor mathematical model gives ideal responses that do not have overshoots and oscillations. For inputs with different speed reference values the results of the system responses generated are shown in Figure 10(a) and Figure 10(b). The rule base obtained by using 10 generations in the learning process of the fuzzy rule base with the evolutionary algorithm does not represent a sufficiently defined fuzzy control to control the dc motor under load. ...
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... this case, although the dc motor output speed signal reach to the desired reference speed value in a short time, then in steady state occured some oscillations. As shown in Figure 10(c), oscillations at the output increase when high reference values are selected for the motor speed. ...
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... highest fitness values obtained in each generation in the learning process with the evolutionary algorithm are given in Figure 11. The unit step response for the rule base with the highest fitness value achieved over 30 generations is shown in Figure 12 for dc motor control system. ...
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... highest fitness values obtained in each generation in the learning process with the evolutionary algorithm are given in Figure 11. The unit step response for the rule base with the highest fitness value achieved over 30 generations is shown in Figure 12 for dc motor control system. ...
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... the genetic-based fuzzy system, firstly using 30 generation and acquired the rule base with has the highest fitness value was applied in the fuzzy-PI controller to drive the dc motor at different reference input values. When looking at the Fuzzy-PI dc motor control system response shown in Figure 13, It is seen that the rise time for the DC motor speed to settle at the desired reference value is less than 1 second, and the settling time is 1.2 seconds. (a) (b) Figure 13. ...
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... looking at the Fuzzy-PI dc motor control system response shown in Figure 13, It is seen that the rise time for the DC motor speed to settle at the desired reference value is less than 1 second, and the settling time is 1.2 seconds. (a) (b) Figure 13. For 30 generations, the fuzzy-PI system responses of dc motor control with load a) for 750 rpm, b) for 1000 rpm. ...
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This study focused on the development of a system based on evolutionary Algorithms to obtain the optimum parameters of the fuzzy controller to increase the convergence speed and accuracy of the controller. The aim of the study is to design fuzzy controller without expert’s knowledge by using evolutionary genetic algorithms and carry out on a DC mot...
