Abstract
The effectiveness of the model structure for designing control system is highly depending on the right selection of tuning parameters belonging of the algorithms. No doubt, preferable parameter estimation leads to better results for the modelling process. This study consists of two main parts. In the first part, fractional and integer model structures with time delay are proposed for integer-order systems using artificial bee colony (ABC) algorithm and differential evolution (DE) algorithm. The second part of the study is the fractional order PID controller design based on the use of new models obtained with the help of Matlab/Simulink software package. While the integrated square error (ISE) is preferred in the modelling process as the performance criterion, four different performance indices are chosen as ISE, integral time-square error (ITSE), integral absolute error (IAE) and integral time-weighted absolute error (ITAE) during the controller design phase. It is shown that, the results obtained with fractional modelling have achieved better results than the integer order modelling. Furthermore, the controller designs for the algorithm based models proposed in the first stage of the study present a satisfactory performance.DOI: http://dx.doi.org/10.5755/j01.eie.24.5.21836
Highlights
Learning of internal structure that characterizes the system by using experimental or mathematical data is called as system modelling [1]–[4]
fractional order proportional-integral-derivative (FOPID) controller designs have been implemented for these models based new systems that reflect the characteristics of the processes
As a result of the study it is possible to say the following: The fractional order model structures proposed for highorder and oscillatory systems that are relatively difficult to identify by a model, have achieved successful results
Summary
Learning of internal structure that characterizes the system by using experimental or mathematical data is called as system modelling [1]–[4]. One of the effective methods of the linear and nonlinear system identification is to use the adaptive algorithms or artificial intelligence techniques. In the literature, it has been involved in a lot of work related to modelling in recent years [1]–[10]. Mete et al [5] presented system modelling based on cascade of a nonlinear second order volterra (SOV) model and a linear FIR model using DE algorithms. Another study which is examination of ABC algorithm performance in the modelling of higher order systems is presented by Bagis and Senberber [4]. In the study of Deng, a system modelling approach based on PSO algorithm is reported [6]. Chaudhary and Raja [7] developed a type of fractional order the LMS (least mean square) algorithm for system identification of Box-Jenkins
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