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Structural Engineering and Mechanics
  Volume 83, Number 4, August25 2022 , pages 537-549

A novel multi-feature model predictive control framework for seismically excited high-rise buildings
Javad Katebi, Afshin Bahrami Rad and Javad Palizvan Zand

    In this paper, a novel multi-feature model predictive control (MPC) framework with real-time and adaptive performances is proposed for intelligent structural control in which some drawbacks of the algorithm including, complex control rule and non-optimality, are alleviated. Hence, Linear Programming (LP) is utilized to simplify the resulted control rule. Afterward, the Whale Optimization Algorithm (WOA) is applied to the optimal and adaptive tuning of the LP weights independently at each time step. The stochastic control rule is also achieved using Kalman Filter (KF) to handle noisy measurements. The Extreme Learning Machine (ELM) is then adopted to develop a data-driven and real-time control algorithm. The efficiency of the developed algorithm is then demonstrated by numerical simulation of a twenty-story high-rise benchmark building subjected to earthquake excitations. The competency of the proposed method is proven from the aspects of optimality, stochasticity, and adaptivity compared to the KF-based MPC (KMPC) and constrained MPC (CMPC) algorithms in vibration suppression of building structures. The average value for performance indices in the near-field and far-field (El earthquakes demonstrates a reduction up to 38.3% and 32.5% compared with KMPC and CMPC, respectively.
Key Words
    adaptive control; data-driven control; Extreme Learning Machine (ELM); Model Predictive Control (MPC); stochastic control; Whale Optimization Algorithm (WOA)
Javad Katebi, Afshin Bahrami Rad and Javad Palizvan Zand: Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

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