Robust semi-active control for uncertain structures and smart dampers

Fallah, Arash Yeganeh; Taghikhany, Touraj

doi:10.1088/0964-1726/23/9/095040

Cited by 14 publications

(11 citation statements)

References 26 publications

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“…Adaptive neuro‐FIS uses the given input/output data sets to generate an FIS, which its membership function parameters are tuned by hybrid algorithm. To select the appropriate training data sets, previous research works concerning to generate the neuro‐fuzzy–based inverse model of MR dampers were reviewed(–) and positive points of different training ideas were utilized to generate our different input/output training data sets that are shown in Figure . The accuracy of the inverse model is improved by increasing the input data.…”

Section: Inverse Adaptive Neural Fuzzy System Model Of Mr Dampermentioning

confidence: 99%

Online self‐tuning mechanism for direct adaptive control of tall building

Hosseini

Taghikhany

2017

Adaptive Control & Signal

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to the control system has completely different characteristics than the initial designed seismic excitation. KEYWORDS adaptive control, ANFIS, fuzzy inference system, MR damper, SAC, semiactive control 424 TABLE 6 Evaluation criteria comparison of different designed fuzzy inference systems Kobe J 1 J 2 J 3 J ave 3-Layer 0.7836 0.5857 0.7897 0.7196 5-Layer 0.7749 0.5784 0.8027 0.7187 7-Layer 0.7676 0.5713 0.8154 0.7181 El Centro J 1 J 2 J 3 J ave 3-Layer 0.7841 0.7502 0.9396 0.8246 5-Layer 0.7504 0.733 0.9268 0.8034 7-Layer 0.7088 0.7147 0.936 0.7865

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Section: Inverse Adaptive Neural Fuzzy System Model Of Mr Dampermentioning

confidence: 99%

Online self‐tuning mechanism for direct adaptive control of tall building

Hosseini

Taghikhany

2017

Adaptive Control & Signal

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“…So as to optimize control forces,

\tilde{y} = F x (t)

is augmented with regulated output as

\bar{z} = {[{\overset{y}{true}}^{T} Z^{T}]}^{T}

. To design feedback gain in

H_{\infty}

structure, the L 2 gain from w to

\overset{z}{true}

, which equals to

H_{\infty}

norm of the transfer function

G_{\overset{z}{true} w}

, is defined as (Fallah and Taghikhany, ):

\underset{F stabilizing}{falseprefixmin} \underset{w}{sub} \frac{{∥ z (t) ∥}_{2}}{{∥ w (t) ∥}_{2}} = \underset{F stabilizing}{falseprefixmin} ∥ G_{\overset{z}{true} w} ∥_{\infty}

…”

Section: Fault Tolerant Controllermentioning

confidence: 99%

“…In passive fault‐tolerant control systems (PFTCS), a robust controller is designed with respect to the predefined faults (Fallah and Taghikhany, ; Battaini, ). Kim and Adeli in 2004 presented the robust algorithm which integrated linear quadratic Gaussian (LQG) controller with the filtered‐x linear mean square (LMS) and used a wavelet‐hybrid feedback.…”

Section: Introductionmentioning

confidence: 99%

A Modified Sliding Mode Fault Tolerant Control for Large‐Scale Civil Infrastructure

Fallah

Taghikhany

2015

Computer aided Civil Eng

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Adaptable active control strategies besides advance sensors and actuators technologies lead to higher performance of vibrational control in civil infrastructures under severe ground motions. These resilience control systems are robust against model uncertainties as well as being online recoverable from the malfunctioning of sensors and actuators. In this study, resilient control system based on sliding mode (SM) fault detection observer and SM fault tolerant control is improved for actuator fault in large‐scale systems. The SM fault detection observer is modified for eliminating the excessive chattering in estimating states and actuators’ fault, and the reconfigurable SM fault tolerant control is improved to minimizing input forces in H∞ control framework under seismic action. Design of observer and controller is performed using linear matrix inequalities. Numerical simulations on the cable‐stayed bridge benchmark demonstrate the effectiveness of the proposed fault‐tolerant system. Despite the high order of this large‐scale structure, the proposed fault detection and diagnosis method can effectively find the location and size of faults in actuators without performance degradation and computational costs. The fault‐tolerant controller maintains the performance of the structure at an acceptable level in the post‐fault case by redistribution of control signal to actuators.

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“…28,29 These linear fixed controllers lack adaptivity towards parametric uncertainties, unmodeled dynamics uncertainties and external disturbances make them completely ineffective in controlling nonlinear civil structures. 30,31 To overcome adaptivity issues, nonlinear adaptive control is recommended for controlling civil structures. 16,[32][33][34] These controllers have shown high adaptivity under highly uncertain and unknown conditions by automatically adjusting their parameters in real-time.…”

Section: Introductionmentioning

confidence: 99%

Research developments in adaptive intelligent vibration control of smart civil structures

Saeed

Sun

Elias

2021

Journal of Low Frequency Noise, Vibration and Active Control

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Control algorithms are the most critical aspects in the successful control of civil structures subjected to earthquake and wind forces. In recent years, adaptive intelligent control algorithms are emerging as an acceptable substitute method to conventional model-based control algorithms. These algorithms mainly work on the principles of artificial intelligence (AI) and soft computing (SC) methods that make them highly efficient in controlling highly nonlinear, time-varying, and time-delayed complex civil structures. The current research probes to control algorithms, that this article set forth an inclusive state-of-the-art review of adaptive intelligent control (AIC) algorithms for vibration control of smart civil structures. First, a general introduction to adaptive intelligent control is presented along with its advantages over conventional control algorithms. Second, their classification concerning artificial intelligence and soft computing methods is provided that mainly consists of artificial neural network-based controller, brain emotional learning-based intelligent controller, replicator dynamics-based controller, multi-agent system-based controller, support vector machine-based controller, fuzzy logic control, adaptive neuro-fuzzy inference system-based controller, adaptive filters-base controller, and meta-heuristic algorithms-based hybrid controllers. Third, a brief review of these algorithms with their developments on the theory and applications is provided. Fourth, we demonstrate a summarized overview of the cited literature with a brief trend analysis is presented. Finally, this study presents an overview of these innovative AIC methods that can demonstrate future directions. The contribution of this article is the anticipation of detailed and in-depth discussion into the perspective of AI and SC-based AIC method advances that enabled practical applications in attenuating vibration response of smart civil structures. Moreover, the review demonstrates the computing advantages of AIC over conventional controllers that are important in creating the next generation of smart civil structures.

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Robust semi-active control for uncertain structures and smart dampers

Cited by 14 publications

References 26 publications

Online self‐tuning mechanism for direct adaptive control of tall building

Online self‐tuning mechanism for direct adaptive control of tall building

A Modified Sliding Mode Fault Tolerant Control for Large‐Scale Civil Infrastructure

Research developments in adaptive intelligent vibration control of smart civil structures

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