Intelligent techniques for system Identification and controller tuning

Abstract

Proportional Integral Derivative PID controller has been widely newlineused in the process industry for many years Tuning of PID controller newlineparameters is necessary for the satisfactory operation of the process In the newlinecommonly used Ziegler Nichols ZN and Cohen Coon CC methods the newlineparameters of the controller are obtained for an operating point where the newlinemodel can be considered linear Internal Model Control IMC overcomes newlinethis problem but its design calculations could be complicated for higher newlineorder process In general plant parameters change due to ageing of the plant or newlinechanges in the load Also the process non linearities and time dependent newlinecharacteristics cause a significant change in the dynamic parameters of the newlineprocess which necessitates identification of the process model at different newlineoperating conditions so that controller design can be effected The newlineconventional parameter identification methods namely least squares and newlinemaximum likelihood method often fail in the search for global optimum in newlinethe search space Further they require large set of input output data from newlinethe system Intelligent techniques overcome the difficulties and limitations newlineencountered by the conventional approaches for system identification and newlinecontroller tuning newline newline