Optimal linear control based on genetic algorithms for flow regulation in a pilot test bank

• Gustavo Colmenarez Universidad Privada Dr. Rafael Belloso Chacín; 2Universidad del Zulia
• Kenneth Rosillon Universidad Privada Dr. Rafael Belloso Chacín; 2Universidad del Zulia

DOI: https://doi.org/10.5281/zenodo.13174986

Keywords: Genetic Algorithms, Test Bench, Linear Optimal Control, Flow, Multi-objective Optimization

Abstract

The main objective of this research was to propose a linear optimal controller based on genetic algorithms for flow regulation in a pilot test bench for pneumatic valves of the instrumentation and control laboratory of the School of Mechanical Engineering at the University of Zulia. It was theoretically supported by (Ogata K., 1996), (Ogata K., 2010), (Aström & Hägglund, 2009), (Ljung, 1998), (Holland, 1992), (Mitchell, 1996), (Goldberg , 1989), for the study variable: Linear Optimal Controller based on Genetic Algorithms. The methodology used in the research was descriptive, non-experimental design. The research consisted of four phases. Initially the Description of the operation of the pilot test bench, followed by Mathematical modeling of the flow process in the pilot test bench, then the Design of a linear optimal controller based on genetic algorithms and finally Validation through simulations of the behavior of the linear optimal control based on genetic algorithms. The results were obtained regarding the identification of systems and determination of the mathematical model, that the system adjusted in its dynamics to the BJ11220 structure, being a second order system yielding approximately 82.77% adjustment. Likewise, the structure of the simple genetic algorithm was adapted to tune the calculation parameters of the linear optimal control, resulting in the system converging to an optimal solution in a limited time. Finally, these results were compared with several control architectures designed for the test bench where satisfactory results were obtained by showing a faster response and with less error in all cases.

 

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