Title: Development of modified LQG controller for mitigation of seismic vibrations using swarm intelligence

Authors: Gaurav Kumar; Roshan Kumar; Ashok Kumar; Brij Mohan Singh

Addresses: Department of Electronics and Communication Engineering, ACED, Alliance University, Bengaluru, Karnataka 562106, India ' School of Electronic and Information Technology, Miami College of Henan University, Henan, China ' Department of Earthquake Engineering, Indian Institute of Technology, Roorkee, Uttarakhand, India ' Department of Computer Science and Engineering, College of Engineering Roorkee, Uttarakhand, India

Abstract: A method is presented to design and tune the modified linear quadratic Gaussian (LQG) controller to obtain increased efficiency during an earthquake. It utilises swarm intelligence to tune the parameters of LQG based on quasi resonance between the natural frequencies of the structure in first two modes and the predominant frequencies of the seismic signal. The modified controller thus developed minimises the energy of structure by altering its parameters online. For testing of this modified controller, a benchmark prototype structure is numerically tested under different seismic signatures recorded in near/far fault sites in the different soil conditions. A parametric study comparing the efficiencies of modified LQG, and other contemporary controllers is presented. It is observed for El-Centro earthquake that the modified controller achieved reductions of 22%, 33% and 27% in relative displacement, inter-storey drift, and absolute acceleration respectively as compared to the conventional LQG controller. Similar results are observed for Gebze and Chi-Chi earthquakes. The modified controller is also evaluated in a situation where power vanishes at the peak of the seismic excitation. Based on the results and discussion, the performance of the proposed controller is observed to be superior among all controllers considered in this study.

Keywords: semi-active control; magneto-rheological damper; seismic vibrations; optimal control; particle swarm optimisation; linear quadratic Gaussian; LQG.

DOI: 10.1504/IJAAC.2023.127274

International Journal of Automation and Control, 2023 Vol.17 No.1, pp.19 - 42

Received: 20 Mar 2021
Accepted: 05 Aug 2021
Published online: 30 Nov 2022 *