Two Types Of External Linear Models Used For Adaptive Control Of Continuous Stirred Tank Reactor

Abstract

The main goal of this contribution is to show simulation results for two types of External Linear Models (ELM) used in the adaptive control as a linear representation of the originally nonlinear system. The nonlinearity is dispathed with the use of recursive identification which recomputes parameters of ELM in each step according to actual state of the system. The controller design employes polynomial approach with pole-placement method and spectral factorization and all these techniques together satisfies basic control requirements. The verification was done on the mathematical model of Continuous Stirred Tank Reactor (CSTR) as a typical member of the nonlinear equipment widely used in chemical industry.

Continuous-Time Vs. Discrete-Time Identification Models Used For Adaptive Control Of Nonlinear Process

“…One disadvantage of this method is that it is very general and it provides for example for deg d(s) = 4 four simple roots, two double roots, one single and one triple root but no recommendation for the choice of these roots. Our previous experiment (Vojtesek and Dostal, 2011) have shown, that it is good co connect the choice of this polynomial somehow with the controlled system. The Spectral factorization could be used for this task and it means that the polynomial d(s) is divided into two parts ( ) ( ) ( )…”

“…The second approach used for example in (Vojtesek and Dostal, 2011) uses so called delta-models for identification. The delta-models are special types of DT models where input and output variables are related to the sampling period.…”

“…It was proved that parameters of the delta-model approach to parameters of the CT model for sufficiently small sampling period (Stericker and Sinha, 1993). This combination of the continuoustime control synthesis with the discrete-time identification is called "Hybrid adaptive control" and some applications can be found for example in (Vojtesek and Dostal, 2005) and (Vojtesek and Dostal, 2011). The second way is to use the CT control synthesis and also the CT recursive identification.…”

Comparison of predictive control using Self-Organizing Migrating Algorithm and MATLAB fmincon function

“…The state of art of chemical reactors control presents for example Luyben in [3] and [4], control and monitoring of batch reactors also describes Caccavale et al in [5]. Generally, it can be stated that chemical reactors controllers use various control methods, such as PI controllers, adaptive control methods, robust approaches, predictive control and the like [6][7][8][9][10][11][12][13][14][15]. The model predictive control [16][17][18] belongs to the one of the most popular and successful approaches for semi-batch reactors control.…”

Self - Organizing Migrating Algorithm in Model Predictive Control: Case study on Semi-Batch Chemical Reactor

“…The state of art of chemical reactors control presents Luyben in [13] and [14], control and monitoring of batch reactors describes Caccavale et al in [15]. Generally, it can be stated that chemical reactors controllers uses various control methods, such as PI controllers, adaptive control methods, robust approaches, predictive control and the like [16][17][18][19][20][21][22][23][24][25]. The model predictive control [26][27][28] belongs to the one of the most popular and successful approaches for semi-batch reactors control.…”