A shuffled frog-leaping algorithm based mixed-sensitivity H∞ control of a seismically excited structural building using MR dampers

Abstract

An intelligent robust controller, which combines a shuffled frog-leaping algorithm (SFLA) and an H∞ control strategy, is designed for a semi-active control system with magnetorheological (MR) dampers to reduce seismic responses of structures. Generally, the performance of mixed-sensitivity H∞ (MSH) control highly depends on expert experience in selecting the parameters of the weighting functions. In this study, as a recently-developed heuristic approach, a multi-objective SFLA with constraints is adopted to search for the optimal weighting functions. In the proposed semi-active control, firstly, based on the Bouc–Wen model, the forward dynamic characteristics of the MR damper are investigated through a series of tensile and compression experiments. Secondly, the MR damper inverse model is developed with an adaptive-network-based fuzzy inference system (ANFIS) technique. Finally, the SFLA-optimized MSH control approach integrated with the ANFIS inverse model is used to suppress the structural vibration. The simulation results for a three-story building model equipped with an MR damper verify that the proposed semi-active control method outperforms fuzzy control and two passive control methods. Besides, with the proposed strategy, the changes in structural parameters and earthquake excitations can be satisfactorily dealt with.

Comparative study between backstepping and backstepping sliding mode controller for suspension of vehicle with a magneto-rheological damper

“…This semi-active device behaves similarly to a hydraulic damper, with the exception that its fluid can modify its yield point when such an external magnetic field. Ultimately, MR dampers are strong devices that combine the benefits of passive components with the advantages of active control [24], [27], [28].…”

Modeling of a magneto-rheological damper: An improved multi-state-dependent parameter estimation approach

“…The smart fluid starts to change to semi-solid when the current is gradually increased. Overall, the MR dampers are powerful devices having the advantages of passive devices with the benefits of active control (Bathaei et al, 2018; Dyke et al, 1996, 1998; Lin et al, 2018; Spencer et al, 1997; Uz and Hadi, 2014; Yi et al, 2001).…”

“…The magneto-rheological (MR) damper is a popular smart damping device in the field of semi-active control of structures (Bathaei et al, 2018; Casciati et al, 2012; Lin et al, 2018; Nguyen et al, 2018; Talatahari and Rahbari, 2015; Uz and Hadi, 2014; Weber, 2014). Having low power requirements, this device, controlled by a magnetic field, plays an important role in vibration suppression in civil and mechanical engineering problems.…”

Parametric Estimation From Empirical Data Using Particle Swarm Optimization Method for Different Magnetorheological Damper Models

“…In the application of MR fluid, bio-inspired computing optimizing algorithm has gained interest as it is a promising approach that can be further developed for new and robust competing techniques. Apart from the Genetic Algorithm, other bio-inspired algorithms, including Grasshopper optimization algorithm [27], shuffled Frog Leaping Algorithm [28], and Crow Search Algorithm [29] have shown potential in describing the MR damper used. However, these algorithms are complicated.…”