Probabilistic Active Control of Structures using a Probabilistic Fuzzy Logic Controller

Document Type : Regular Paper


1 PhD student, Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran,

2 Faculty Member

3 Professor, Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

4 Assistant Professor, Faculty of Civil Engineering and Environment, Khavaran institute of Higher Education of Mashhad, Mashhad, Iran


Because uncertainty is inherent in engineering structures, it is essential to improve the procedures of structural control. The present study investigated applying a probabilistic fuzzy logic system (PFLS) in active tendons for the covariance response control of buildings. In contrast to an ordinary fuzzy logic system, PFLS integrates probabilistic theory into a fuzzy logic system that can handle the linguistic and stochastic uncertainties existing in the process. To investigate the proficiency of the suggested controller, a single degree of freedom (SDOF) system and a three-story multiple degree of freedom (MDOF) system with different arrangements of tendons on the stories were considered. The structures were subjected to a random dynamic load modeled using Gaussian white noise, and the modeling parameters the damping, stiffness, and mass were considered to be random Gaussian samples with a dispersion coefficient of 10%. The results calculated by the suggested intelligent control scheme were evaluated with those of an uncontrolled structural model and model with a linear quadratic regulator (LQR) controller. The numerical finding revealed that the probabilistic fuzzy logic controller (PFLC) was very efficient in decreasing the structural covariance responses relative to the LQR controller. Moreover, the most and least reduction values of displacement responses for MDOF structures were about 36% and 12.5%, respectively, compared to the LQR controller. It is also showed that the PFLC is more accurate because it includes stochastic uncertainty.


Main Subjects

Articles in Press, Accepted Manuscript
Available Online from 27 June 2021
  • Receive Date: 09 January 2021
  • Revise Date: 26 May 2021
  • Accept Date: 27 June 2021