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Smart vibration control of structures with unknown structural parameters using integrated virtual synchronization method/linear‐quadratic regulator approach
Author(s) -
Ghaderi Pedram,
Goshtaei Seyed Matin
Publication year - 2020
Publication title -
advanced control for applications: engineering and industrial systems
Language(s) - English
Resource type - Journals
ISSN - 2578-0727
DOI - 10.1002/adc2.48
Subject(s) - linear quadratic regulator , control theory (sociology) , vibration , vibration control , synchronization (alternating current) , actuator , quadratic equation , stiffness , engineering , computer science , optimal control , mathematics , control (management) , structural engineering , mathematical optimization , topology (electrical circuits) , physics , quantum mechanics , electrical engineering , artificial intelligence , geometry
Although vibration control and health monitoring of structures have been treated separately in recent years, it will be beneficial to integrate these two systems and develop a smart structure with its own sensors, processors, and actuators. In this article, an integrated virtual synchronization method/linear‐quadratic regulator (VSM/LQR) approach for system identification and vibration control of structures with unknown physical parameters is proposed. First, based on the measured state of the structure, the unknown structural parameters are estimated using the VSM in a relatively short duration. The estimated parameters are then employed to determine optimal control forces for the purpose of the vibration control. The feasibility and effectiveness of the proposed approach are numerically examined through the time‐history analysis of two shear‐type frames with unknown stiffness coefficients. The results from the numerical investigation of the example structures demonstrate that the proposed method is effective in the vibration attenuation of structures with unknown parameters. Moreover, it was shown that the proposed VSM/LQR approach is more promising for addressing unknown structural parameters than the recursive least square method and conventional LQR method.