Nowadays, the PID controller is very common controller as well as very important controller in industrial utilizations. In the paper, proposed an ALO algorithm and ANN controller is utilized to enhance PID controller performance and control the tuning of TDS. TDS stands for Time delay system. ALO stands for Ant lion optimizer and ANN stands for Artificial neural network. In terms of parameters controlling, the time delay system is controlled and for different delay events low overshoot and fast time settling is reached. The novelty of the presented method is enhancing the PID controller performance by optimizing the PID gain parameters and controlling the highorder TDS. The performance of time delay system can be enhanced through decreasing error, tracking, time delay & error, rapid and exactly for their corresponding reference values. For parameter controlling of time delay system along optimal values, can be significantly enhanced the performance. To analyze the characteristics of the presented method, the various time delay systems are analyzed. The input and gain parameters were utilized to evaluate the objective function from tuning system. Based on proposed method, the optimal result is achieved and evaluated the increae time, settling time, overshoot as well as steady state error in TDS. The suggested controller is executed in MATLAB/Simulink work site and the presented technique performance examined through performance indexes and time domain specifications are evaluated using presented method compared to previous methods like ABC (Artificial Bee colony) algorithm, GSA (Gravitational Search Algorithm) ,FA (Firefly Algorithm).


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    Title :

    An Effective Technique for Tuning the Time Delay System with PID Controller-Ant Lion Optimizer Algorithm with ANN Technique



    Publication date :

    2020-02-29


    Remarks:

    oai:zenodo.org:5580743
    International Journal of Engineering and Advanced Technology (IJEAT) 9(3) 2421-2429



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629





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