Whale Optimization Algorithm-Based LQG-Adaptive Neuro-Fuzzy Control for Seismic Vibration Mitigation with MR Dampers

Abstract

Design of effective control strategies to protect structural buildings from seismic hazards is gaining increasing attention. In this paper, an intelligent semiactive control strategy, which combines linear-quadratic-Gaussian (LQG), whale optimization algorithm (WOA), and adaptive neuro-fuzzy inference system (ANFIS) strategy is designed to mitigate structural vibration by using magnetorheological (MR) dampers, here known as WLQG-ANFIS control. Firstly, considering that the performance of the LQG control for the structures under seismic excitations strongly depends on skill and experience of the experts in determining the weighting matrix of the feedback gain, a WOA with constraints is adopted to optimally design the LQG control due to the strong ability to avoid local optima. Secondly, the inverse dynamic model of the MR damper is developed with the ANFIS technique which combines the fuzzy system and artificial neural network. Finally, according to the active force estimated by the proposed LQG control, the developed inverse model is utilized to calculate the MR damper command signal. Numerical analyses are conducted for a 10-story structural building installed with MR dampers under earthquake excitation. Through a test called analysis of variance in optimizing the LQG control, the superiority of WOA over other three meta-heuristic algorithms, i.e. GA, DE, and ABC, in terms of convergence performance and robustness is validated. The proposed WLQG-ANFIS control can reduce the maximum displacement, interstory drift and acceleration responses by 64%, 65%, and 53%, respectively, which can achieve better overall performance than on-off control, LQG-ANFIS control, fuzzy control, and H∞-ANFIS control. Moreover, this control strategy exhibits the ability to handle the uncertainties in structural parameters as well as seismic excitations.

Active vibration control using nonlinear auto-regressive neural network to identify secondary channel

“…Neural networks are a class of data-driven general-purpose models with powerful nonlinear fitting capabilities, and various neural network control algorithms have emerged and achieved certain results and applications in many fields. [17][18][19][20][21] Although the modeling capability of neural networks in dynamic systems has been studied as early as 1997, 22 there is less research on the use of neural networks in adaptive feedforward control algorithms for the identification of secondary channels due to the influence of the nonlinear nature of neural networks themselves, which makes the update of controller parameters in the Fx-LMS algorithm compromised.…”