Author, Institution: Mantas Butkus, Kaunas University of Technology
Science area, field of science: Technological Sciences, Informatics Engineering, T007
Scientific Supervisor: Prof. Dr. Vytautas Galvanauskas (Kaunas University of Technology, Technological Sciences, Informatics Engineering, T007)
Dissertation Defence Board of Informatics Engineering Science Field:
Prof. Dr. Rytis Maskeliūnas (Kaunas University of Technology, Technological Sciences, Informatics Engineering, T007) – chairperson
Prof. Dr. Arnas Kačeniauskas (Vilnius Gediminas Technical University, Technological Sciences, Informatics Engineering, T007)
Prof. Dr. Tomas Krilavičius (Vytautas Magnus University, Technological Sciences, Informatics Engineering, T007)
Prof. Dr. Olga Kurasova (Vilnius University, Technological Sciences, Informatics Engineering, T007)
Assoc. Prof. Dr. Rui Oliveira (Universidade NOVA de Lisboa, Portugal, Technological Sciences, Chemical Engineering, T005)
Dissertation defence meeting will be at M7 Hall at The Campus Library of Kaunas University of Technology (Studentų 48–M7, Kaunas)
The doctoral dissertation is available at the library of Kaunas University of Technology (K. Donelaičio g. 20, Kaunas) and on the internet: M. Butkus_disertacija.pdf
Annotation: Biotechnological processes are among the most complex control objects with all the characteristics that make the control difficult: non-linear relationships between process variables, time-varying dynamic properties and lack of sensors that can provide reliable process monitoring. The need for adaptive control algorithms is high, and they are needed to develop new and improve the already available biotechnological processes in both scientific laboratories and industry. The academic community has proposed various adaptive control methods; however, they tend to be complex, and they also require a lot of time and knowledge to develop and fine-tune. In this doctoral dissertation five easy-to-implement adaptive control algorithms that are based on fuzzy logic, gain scheduling, statistical and polynomial analysis and substrate feeding profile adaptation are presented. The main advantages of the developed adaptation techniques can be summarized as: a simple model structure which relies on the process operator’s level of knowledge and basic mathematical operations; usage of only controller input/output signals for controller tuning parameter adaptation; minimization of the required soft-sensor measurements for the realization of controller tuning parameter adaptation. To evaluate the performance of the developed systems, the proposed models were compared with standard PI controllers with fixed parameters or similar adaptive control techniques.
June 16 d. 10:00
M7 Hall at The Campus Library of Kaunas University of Technology (Studentų 48–M7, Kaunas)
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