APPLICATION OF CLONAL SELECTION ALGORITHM FOR PID CONTROLLER SYNTHESIS OF MIMO SYSTEMS IN OIL AND GAS INDUSTRY
This paper presents the results of the Smart technologies application to the synthesis of MIMO-systems in oil and gas industry. In particular, there is considered a multidimensional multiply connected system for gas distillation process control through a distillation column with regulators configured on the basis of Smart-technologies – clonal selection algorithm (CLONALG) of an artificial immune system (AIS).
artificial immune system (AIS); clonal selection algorithm (CLONALG); PID-controller; MIMO-system
Arain B.A., Shaikh M.F., Harijan B.L., Memon T.D., Kalwar I.H.: Design of PID Controller Based on PSO Algorithm and Its FPGA Synthesization. International Journal of Engineering and Advanced Technology 2/2018, 201–207.
Bobikov A.I.: Correction of the weight matrices of the SURSD controller using bioinspired optimization algorithms. Bulletin of the Russian State Technical University 55/2016, 77–83. DOI: https://doi.org/10.21667/1995-4565-2016-55-1-131-139
Burlakov M.E.: Overview of the basic algorithm of the artificial immune system on the theory of negative selection. Ufa: Collection of articles of the International Scientific and Practical Conference 2014, 29–31.
Castro L., Zuben F.: The Clonal Selection Algorithm with Engineering Applications. Proceedings of Workshop GECCO’00, Las Vegas 2000, 36–37.
Chinjiang L.: Optimal design of high-rise building wiring based on ant colony optimization. Cluster Computing 2018, 1–8.
Dasgupta D., Yu S., Nino F.: Recent advances in artificial immune systems: models and applications. Applied Soft Computing Journal 2/2011, 1574–1587. DOI: https://doi.org/10.1016/j.asoc.2010.08.024
Kushnir N.V., Kushnir A.V., Anatskaya A.V., Katysheva P.A., Ustinov K.G.: Artificial immune systems: an overview and state of the art. Scientific works of KubGTU 12/2015, 10.
Li Zh., Huang H., Tan H., Zhang Y.: IA-AIS: An Improved Adaptive Artificial Immune System and Its Application in Tuning of PID Controlled System. Journal of Information & Computational Science 5/2008, 2193–2200.
Litvinenko V.I., Fefelov A.A., Goravsky S.P.: Object-oriented implementation of the clonal selection algorithm. Radio electronics. Computer science. Management 1/2003, 81–88.
Minian F, Sabouhi H., Hushmand J., Hallaj A., Khaledi H., Mohammadpour M.: Gas turbine preventive maintenance optimization using genetic algorithm. International Journal of System Assurance Engineering and Management 8/2016, 594–601. DOI: https://doi.org/10.1007/s13198-017-0627-3
Sahraoui M., Salem M.: Application of artificial immune algorithm-based optimisation in tuning a PID controller for nonlinear systems. International Journal of Automation and Control 3/2015, 186–200. DOI: https://doi.org/10.1504/IJAAC.2015.070955
Saleh M., Saad S.: Artificial Immune System based PID Tuning for DC Servo Speed Control. International Journal of Computer Applications 2/2016, 23–26. DOI: https://doi.org/10.5120/ijca2016912265
Shiryaeva O.I., Samigulin T.I.: Development of SMART-management system of a complex object of the oil and gas industry using the decoupling procedure. Bulletin of KazNRTU 5/2017, 50–55.
Slavov T., Roeva O.: Application of Genetic Algorithm to Tuning a PID Controller for Glucose Concentration Control. WSEAS Transactions on Systems 7/2012, 223–233.
Trebuhin A.V.: Methods for solving optimization problems using bioinspired algorithms. Young Researcher Don, DSTU 6/2017, 108–111.
Wang M., Feng S., He Ch., Li Zh., Yu X.: An Artificial Immune System Algorithm with Social Learning and Its Application in Industrial PID Controller Design. Mathematical Problems in Engineering 2017, 13. DOI: https://doi.org/10.1155/2017/3959474
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.