TY - JOUR

T1 - Novel Adaptive Intelligent Control Scheme with Self-Evolving Genetic Fuzzy BELBIC for Enhancing the Seismic Resilience of Smart Building Structures

AU - Saeed, Muhammad Usman

AU - Elias, Said

AU - Li, Peizhen

N1 - Publisher Copyright: © 2025 American Society of Civil Engineers.

PY - 2025/6/1

Y1 - 2025/6/1

N2 - The most crucial aspect of effective control of civil structures is control algorithms. Under severe earthquake disturbances, the modern semiactively controlled civil structures are highly nonlinear, uncertain, and complex nonstationary systems. These structures require real-time (online) robust control actions to maintain their serviceability and reliability characteristics dynamically toward changing conditions, which the controllers with rigid settings cannot adapt to. Advanced computational methods are needed to develop robust, optimized control to achieve this goal. Adaptive intelligent control algorithms have become a viable substitute for conventional model-based control algorithms. One of the most recent developments, known as the brain emotional learning-based intelligent controller (BELBIC), has caught the attention of scientists as a model-free adaptive control system. It possesses appealing capabilities for dealing with nonlinearities and uncertainties in control frameworks. This paper presents a novel tuning method for the optimum design of a standard brain emotional learning (BEL) controller for smart building structures. The principal contribution of the proposed control scheme is the development of an online self-evolving genetic fuzzy BELBIC (OSEF-BELBIC) that learns an inference (decision-making) system by itself. The proposed control scheme benefits offline and online tuning methods equally: the online self-attuned routines Takagi-Sugeno-Kang-fuzzy inference system and the offline fuzzy inference system tuned by the evolutionary genetic algorithm, also known as the floating fuzzy inference system. In this case, the central control unit BELBIC is based on sensory inputs and emotional cues (reward) signals. Besides, a design methodology is also introduced into the reward signal that combines the classical proportional-integral-derivative (PID) and evolutionary fuzzy logic controller. The proposed control methodology can be a promising model-free adaptive intelligent controller in terms of online and offline BELBIC tuning for the response of each floor in parallel to neutralize nonlinearities. The simulation confirms that the proposed controller, compared with the developed fixed and crisp valued offline tuned genetic PID-based BELBIC (GPID-BELBIC), has superior performance in demonstrating high learning abilities to avoid local minima in attenuating seismic responses of the building.

AB - The most crucial aspect of effective control of civil structures is control algorithms. Under severe earthquake disturbances, the modern semiactively controlled civil structures are highly nonlinear, uncertain, and complex nonstationary systems. These structures require real-time (online) robust control actions to maintain their serviceability and reliability characteristics dynamically toward changing conditions, which the controllers with rigid settings cannot adapt to. Advanced computational methods are needed to develop robust, optimized control to achieve this goal. Adaptive intelligent control algorithms have become a viable substitute for conventional model-based control algorithms. One of the most recent developments, known as the brain emotional learning-based intelligent controller (BELBIC), has caught the attention of scientists as a model-free adaptive control system. It possesses appealing capabilities for dealing with nonlinearities and uncertainties in control frameworks. This paper presents a novel tuning method for the optimum design of a standard brain emotional learning (BEL) controller for smart building structures. The principal contribution of the proposed control scheme is the development of an online self-evolving genetic fuzzy BELBIC (OSEF-BELBIC) that learns an inference (decision-making) system by itself. The proposed control scheme benefits offline and online tuning methods equally: the online self-attuned routines Takagi-Sugeno-Kang-fuzzy inference system and the offline fuzzy inference system tuned by the evolutionary genetic algorithm, also known as the floating fuzzy inference system. In this case, the central control unit BELBIC is based on sensory inputs and emotional cues (reward) signals. Besides, a design methodology is also introduced into the reward signal that combines the classical proportional-integral-derivative (PID) and evolutionary fuzzy logic controller. The proposed control methodology can be a promising model-free adaptive intelligent controller in terms of online and offline BELBIC tuning for the response of each floor in parallel to neutralize nonlinearities. The simulation confirms that the proposed controller, compared with the developed fixed and crisp valued offline tuned genetic PID-based BELBIC (GPID-BELBIC), has superior performance in demonstrating high learning abilities to avoid local minima in attenuating seismic responses of the building.

KW - Brain emotional learning (BEL)-based intelligent controller (BELBIC)

KW - Floating fuzzy

KW - Fuzzy logic

KW - Genetic algorithm

KW - Proportional-integral-derivative (PID)-type fuzzy logic controller

KW - Semiactive vibration control

UR - http://www.scopus.com/inward/record.url?scp=105001338216&partnerID=8YFLogxK

U2 - 10.1061/AJRUA6.RUENG-1464

DO - 10.1061/AJRUA6.RUENG-1464

M3 - Article

AN - SCOPUS:105001338216

VL - 11

JO - ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering

JF - ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering

SN - 2376-7642

IS - 2

M1 - 04025019

ER -