Multivariable nonlinear identification of smart buildings. (October 2015)

• Record Type: Journal Article
• Authors: Kim, Yeesock; Kim, Young Hoon; Lee, Seongsoo
• Is Part Of: Mechanical systems and signal processing. Volume 62/63(2015)
• Page Start: 254
• Page End: 271
• Publication Date: 2015-10
• Subjects: Wavelet transform -- Multi-input–multi-output (MIMO) -- Autoregressive exogenous (ARX) input models -- Takagi–Sugeno (TS) fuzzy model -- Smart structures -- Magnetorheological (MR) damper; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2015.02.010
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: This paper presents a new multi-input–multi-output (MIMO) fuzzy model for nonlinear system identification (SI) of smart structures under a variety of random forces. The fuzzy SI model is developed through the integration of wavelet transform (WT), multiple MIMO linear autoregressive exogenous (ARX) input models, Takagi–Sugeno (TS) fuzzy model, weighted linear least squares, and data clustering algorithms: MIMO WARX-TS fuzzy model. To demonstrate the effectiveness of the MIMO WARX-TS fuzzy model, a three-story building equipped with a magnetorheological (MR) damper under a variety of random signals is investigated. To train the proposed model, an artificial earthquake and control forces are used as input signals while displacement and acceleration responses are used as outputs. To validate the trained model, four real recorded earthquake signals are used. It is shown from the simulation that the proposed MIMO WARX-TS fuzzy identification algorithm is effective in estimating nonlinear behavior of a building–MR damper system under a variety of seismic excitations. Highlights: We propose MIMO nonlinear identification for buildings employing smart dampers. It shows much better performance than an existing model: computations and accuracy. It predicts behavior of smart buildings under various nonstationary random signals. It makes it possible to apply PDC to model-based fuzzy smart control design.