Samopodešavajuće prediktivno funkcionalno upravljanje temperaturom egzotermičkog šaržnog reaktora

In this paper we study a self-adaptive predictive functional control algorithm as an approach to control of temperature in an exothermic batch reactor. The batch reactor is situated in a pharmaceutical company in Slovenia and is used in the production of medicines. Due to mixed discrete and continuous inputs the reactor is considered as a hybrid system. The model of the reactor used for the simulation experiment is explained in the paper. Next, we assumed an exothermic chemical reaction that is carried out in the reactor core. The dynamics of the chemical reaction that comply with the Arrhenius relation have been well documented in the literature and are also summarized in the paper. Furthermore, the online recursive least-squares identification of the process parameters and the self-adaptive predictive functional control algorithm are thoroughly explained. We tested the proposed approach on the batch reactor simulation example that included the exothermic chemical reaction kinetic model. The results suggest that such implementation meets the control demands, despite the strongly exothermic nature of the chemical reaction. The reference is suitably tracked, which results in a shorter overall batchtime. In addition, there is no overshoot of the controlled variable (temperature in the reactor core), which yields a higher-quality production. Finally, by introducing a suitable discrete switching logic in order to deal with the hybrid nature of the batch reactor, we were able to reduce switching of the on/off valves to minimum and therefore relieve the wear-out of the actuators as well as reduce the energy consumption needed for control.U članku se analizira samopodešavajući algoritam prediktivnog funkcionalnog upravljanja kao pristup upravljanju temperaturom egzotermičkog šaržnog reaktora. Šaržni se reaktor nalazi u jednoj slovenskoj farmaceutskoj tvrtki gdje se koristi za proizvodnju medikamenata. Budući da su ulazi u rektor i kontinuirani i diskretni, reaktor je promatran kao hibridni sustav. U članku je opisan model reaktora korišten za simulacije. Nadalje, pretpostavljeno je da se u jezgri reaktora odvija egzotermička reakcija. Opis dinamike kemijske reakcije Arrheniusovim jednadžbama dobro je dokumentiran u literaturi, pa je u članku dan samo kratki pregled. Posebno detaljno opisana je metoda najmanjih kvadrata za procjenu parametara modela te samopodešavajući agoritam prediktivnog funkcionalnog upravljanja. Predloženi pristup upravljanju provjeren je simulacijom na šaržnom reaktoru koji uključuje kinetički model egzoterničke kemijske reakcije. Simulacijski rezultati ukazuju da predloženo upravljanje ispunjava tražene zahtjeve, unatoč jakoj egzotermičkoj naravi kemijske reakcije. Zadane su reference dobro praćene, što rezultira skraćenjem trajanja šaržnog procesa. Osim toga, nepostojanje nadvišenja u temperaturi jezgre reaktora osigurava veću kakvoću proizvodnje. Na koncu, uvođenjem prikladne logike prekapčanja za prilagodbu hibridnoj naravi šaržnog reaktora moguće je značajno smanjiti prekapčanje dvopoložajnih ventila što ima za posljedicu smanjenje njihova trošenja i uštedu u potrošnji energije

Predictive Approaches to Control of Complex Systems

A predictive control algorithm uses a model of the controlled system to predict the system behavior for various input scenarios and determines the most appropriate inputs accordingly. Predictive controllers are suitable for a wide range of systems; therefore, their advantages are especially evident when dealing with relatively complex systems, such as nonlinear, constrained, hybrid, multivariate systems etc. However, designing a predictive control strategy for a complex system is generally a difficult task, because all relevant dynamical phenomena have to be considered. Establishing a suitable model of the system is an essential part of predictive control design. Classic modeling and identification approaches based on linear-systems theory are generally inappropriate for complex systems; hence, models that are able to appropriately consider complex dynamical properties have to be employed in a predictive control algorithm. This book first introduces some modeling frameworks, which can encompass the most frequently encountered complex dynamical phenomena and are practically applicable in the proposed predictive control approaches. Furthermore, unsupervised learning methods that can be used for complex-system identification are treated. Finally, several useful predictive control algorithms for complex systems are proposed and their particular advantages and drawbacks are discussed. The presented modeling, identification and control approaches are complemented by illustrative examples. The book is aimed towards researches and postgraduate students interested in modeling, identification and control, as well as towards control engineers needing practically usable advanced control methods for complex systems