World Scientific
Skip main navigation

Cookies Notification

We use cookies on this site to enhance your user experience. By continuing to browse the site, you consent to the use of our cookies. Learn More
×

System Upgrade on Tue, May 28th, 2024 at 2am (EDT)

Existing users will be able to log into the site and access content. However, E-commerce and registration of new users may not be available for up to 12 hours.
For online purchase, please visit us again. Contact us at customercare@wspc.com for any enquiries.

Online Learning Deep Neural Network Fuzzy Control of Structures Under Earthquake Motions: Numerical and Experimental Tests

    https://doi.org/10.1142/S0219455425501433Cited by:0 (Source: Crossref)

    The conventional control methods typically assume that the controlled structure behaves as a deterministic system, overlooking variations in structural dynamic properties and uncertainties in earthquake motions. To overcome this constraint, this study introduces an innovative approach: an online learning deep neural network fuzzy control method (DFNN). In the proposed method, Offline training was conducted using training samples generated through the Linear–quadratic regulator (LQR) to determine the initial parameters of DFNN. An ON–OFF system was introduced for real-time control signal adjustment. The input and corrected control signals were utilized as training samples to train and modify the parameters of the DFNN system, enabling online learning capabilities. Numerical and experimental investigations were performed to evaluate the effectiveness of passive control (OFF), fuzzy logic control, deep neural network fuzzy control, and online learning deep neural network fuzzy control using a numerical three-story steel frame structure and an experimental two-story steel frame structure with one magneto-rheological (MR) damper. The simulation and shaking table test results demonstrate that DFNN can adaptively adjust the parameters of the neural network, leading to significantly higher control efficiency compared to fuzzy logic control and deep neural network fuzzy control, particularly when the structural properties and earthquake motions differ from the training samples.

    Remember to check out the Most Cited Articles!

    Remember to check out the structures