Developed comparative analysis of metaheuristic optimization algorithms for optimal active control of structures

A developed comparative analysis of metaheuristic optimization algorithms has been used for optimal active control of structures. The linear quadratic regulator (LQR) has ignored the external excitation in solving the Riccati equation with no sufficient optimal results. To enhance the efficiency of...

Full description

Saved in:
Bibliographic Details
Published in:Engineering with computers Vol. 36; no. 4; pp. 1539 - 1558
Main Authors: Katebi, Javad, Shoaei-parchin, Mona, Shariati, Mahdi, Trung, Nguyen Thoi, Khorami, Majid
Format: Journal Article
Language:English
Published: London Springer London 01.10.2020
Springer Nature B.V
Subjects:
Active control
CAE) and Design
Calculus of Variations and Optimal Control; Optimization
Classical Mechanics
Comparative analysis
Computer Science
Computer simulation
Computer-Aided Engineering (CAD
Control
Evolutionary algorithms
Evolutionary computation
Excitation
Heuristic methods
Linear quadratic regulator
Math. Applications in Chemistry
Mathematical and Computational Engineering
Optimization
Optimization algorithms
Original Article
Riccati equation
Systems Theory
Wavelet analysis
Discrete wavelet transform (DWT)
Metaheuristic optimization algorithm
Active control
Linear quadratic regulator (LQR)
ISSN:0177-0667, 1435-5663
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A developed comparative analysis of metaheuristic optimization algorithms has been used for optimal active control of structures. The linear quadratic regulator (LQR) has ignored the external excitation in solving the Riccati equation with no sufficient optimal results. To enhance the efficiency of LQR and overcome the non-optimality problem, six intelligent optimization methods including BAT, BEE, differential evolution, firefly, harmony search and imperialist competitive algorithm have been discretely added to wavelet-based LQR to seek the attained optimum feedback gains. The proposed approach has not required the solution of Riccati equation enabling the excitation effect in controlling process. Employing this advantage by each of six mentioned algorithms to three-story and eight-story structures under different earthquakes led to define (1) the best solution, (2) convergence rate and (3) computational effort of all methods. The purpose of this research is to study the aforementioned methods besides the superiority of ICA in finding the optimal responses for active control problem. Numerical simulations have confirmed that the proposed controller is enabling to significantly reduce the structural responses using less control energy compared to LQR.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0177-0667
1435-5663
DOI:10.1007/s00366-019-00780-7