Seismic-vibration mitigation of a nonlinear structural system with an ATMD through a fuzzy PID controller

Güçlü, Rahmi; Yazıcı, Hakan

doi:10.1007/s11071-009-9500-5

Cited by 45 publications

(14 citation statements)

References 19 publications

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“…The 1940 El Centro Earthquake record was used as input earthquake motions. Data obtained from accelerometers during earthquake are low-and high-pass filtered using the software MATLAB, modeled as a function of time after filtration of the disruptive noise from the recorded signals [16][17][18][19][20].…”

Section: Applying Earthquake Motion To the Mathematical Modelmentioning

confidence: 99%

Investigation of Seismic Behavior of Container Crane Structures by Shake Table Tests and Mathematical Modeling

Azeloglu

Edinçliler

Sagirli

2014

Shock and Vibration

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This paper is concerned with the verification of mathematical modeling of the container cranes under earthquake loadings with shake table test results. Comparison of the shake table tests with the theoretical studies has an important role in the estimation of the seismic behavior of the engineering structures. For this purpose, a new shake table and mathematical model were developed. Firstly, a new physical model is directly fixed on the shake table and the seismic response of the container crane model against the past earthquake ground motion was measured. Secondly, a four degrees-of-freedom mathematical model is developed to understand the dynamic behaviour of cranes under the seismic loadings. The results of the verification study indicate that the developed mathematical model reasonably represents the dynamic behaviour of the crane structure both in time and frequency domains. The mathematical model can be used in active-passive vibration control studies to decrease structural vibrations on container cranes.

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Section: Applying Earthquake Motion To the Mathematical Modelmentioning

confidence: 99%

Investigation of Seismic Behavior of Container Crane Structures by Shake Table Tests and Mathematical Modeling

Azeloglu

Edinçliler

Sagirli

2014

Shock and Vibration

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“…Thus, studying nonlinearity in dynamical systems has been a necessary area of research in the last decades. Meanwhile, vibration mitigation and performance enhancement of nonlinear systems under excitations have drawn the attention of researchers recently . The necessity of reducing vibrations with various control systems including passive, active, and semiactive has made the researchers to develop different control schemes.…”

Section: Introductionmentioning

confidence: 99%

Orthogonal function‐based equivalent linearization for sliding mode control of nonlinear systems

Baghaei

Ghaffarzadeh

Younespour

2019

Struct Control Health Monit

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This paper proposes the equivalent linearization (EL) and sliding mode control (SMC) methods to address nonlinearity and enhance the performance of nonlinear systems subjected to nonstationary random excitations. The EL methods are commonly used to propose approximate solutions for nonlinear systems under random excitations due to the high computational demands in the exact analyses. Considering the application of orthogonal functions in improving the accuracy and efficiency of approximate solutions, an orthogonal block pulse (BP) function is applied to the EL method in this study to approximate the nonlinear system responses under nonstationary random excitations. The SMC, as a robust control method for systems with uncertainties and external disturbances, is capable of achieving reliable and accurate tracking control. This method is applied to effectively reduce the dynamic responses predicted by the proposed EL method under nonstationary random excitations. The proposed approach is tested on single-degreeof-freedom and two-degree-of-freedom Duffing systems subjected to a seismic type excitation. The results indicate that not only the orthogonal functionbased EL method can approximate the dynamic responses more accurately, at lower computational cost, and by a high agreement with the exact solution, but also the proposed SMC can improve the performance of nonlinear systems by effectively reducing the responses compared with the linear quadratic regulator control method.KEYWORDS block pulse functions, equivalent linearization, nonlinearity, sliding mode control Nomenclature: A, plant matrix of the system; B,B u , control location matrices; B r , excitation orientation vector; C,C eq , damping matrix and its equivalent value; E[·], mathematical expectation; g, nonlinear function of displacement and velocity; h, unit impulse-response function; J, cost function; J G ,J F , convolution operational matrix; K,K eq , stiffness matrix and its equivalent value; k, linear stiffness; M, mass matrix; m, positive integer value; P, sliding surface coefficient vector; Q,R, positive semidefinite and definite weighting matrices; q, width of block pulse

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“…Fuzzy gain scheduling is also an effective approach to control systems with variable dynamics . It has been successfully employed for online control of active structural control systems . However, performance of the fuzzy gain‐scheduling controller is very sensitive to the parameters of the fuzzy controller.…”

Section: Introductionmentioning

confidence: 99%

“…25,26 It has been successfully employed for online control of active structural control systems. [27][28][29][30] However, performance of the fuzzy gain-scheduling controller is very sensitive to the parameters of the fuzzy controller. In this case, a fuzzy gainscheduling controller tuned for a given disturbance may not work optimally in confrontation with the other disturbances.…”

Section: Introductionmentioning

confidence: 99%

Online control of an active seismic system via reinforcement learning

Khalatbarisoltani

Soleymani

Khodadadi

2018

Struct Control Health Monit

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Summary Tuning the seismic control systems in order to achieve optimal performance is a challenging area due to the system and disturbance uncertainties. Although, model uncertainties, process time delay, and actuator dynamics can be considered as typical uncertainties, the main source of uncertainty in a seismic control system comes from the aleatory nature of earthquake disturbances. In this case, tuning of the control system based on a given seismic record may not necessarily result in optimal performance for other earthquakes. In this paper, a methodological approach is proposed for online control of active structural control systems considering seismic uncertainties. For this purpose, the concept of reinforcement learning is utilized for online tuning of an active mass drive system. The controller comprises a gain‐scheduling fuzzy proportional derivative controller whose gains are tuned via an online reinforcement learning algorithm. Moreover, in order to tackle the time delays, a dynamic state predictor is utilized in conjunction with the proposed controller. To evaluate the performance of proposed controller, according to an assumed site hazard, thousand ground motion records are generated and clustered based on their spectral features using a fuzzy clustering approach. Finally, the controller is implemented in a laboratory‐scale structure, and its performance is examined in the presence of the cluster centers and some real seismic records simulated on a shake table. The test results reveal successful performance of the proposed controller in tackling a wide range of seismic disturbances in the presence of time delay.

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Seismic-vibration mitigation of a nonlinear structural system with an ATMD through a fuzzy PID controller

Cited by 45 publications

References 19 publications

Investigation of Seismic Behavior of Container Crane Structures by Shake Table Tests and Mathematical Modeling

Investigation of Seismic Behavior of Container Crane Structures by Shake Table Tests and Mathematical Modeling

Orthogonal function‐based equivalent linearization for sliding mode control of nonlinear systems

Online control of an active seismic system via reinforcement learning

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