Active vibration control of building structures using fuzzy proportional-derivative/proportional-integral-derivative control

Thenozhi, Suresh; Yu, Wen

doi:10.1177/1077546313509127

Cited by 29 publications

(16 citation statements)

References 29 publications

(26 reference statements)

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“…Notable algorithms include linear quadratic regulator (LQR) [3], acceleration feedback control [4], H-infinity control [5]. More recent developments include bilinear pole-shifting algorithm [6], fuzzy PID control [7]. Practical implementations of these smart structures in Japan have been summarized [8].…”

Section: Introductionmentioning

confidence: 99%

Active scissor-jack for seismic risk mitigation of building structures

2019

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Active vibration control systems involve sensing and actuation systems to be integrated into a structure. The actuator generates control forces based on sensed external excitation and system response and applies forces directly to the structure in order to reduce its seismic response. One major obstacle with the active vibration control technique is that very large actuator power is often required. This paper studies the effects of positioning an actuator in a scissor-jack configuration within a structural frame. Based on its geometries, the governing equation of motion is derived. A classical optimal control algorithm determines control forces. In a numerical example, a three-level multi-degree of freedom system frame equipped with scissor-jack actuators is compared to an active-tendon system with the same structural characteristics. The results indicate that peak actuator force reduces by 92%. The results indicate that by using the proposed configuration, significant reduction of control forces can be achieved, implying that a much smaller actuator can be used.

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Section: Introductionmentioning

confidence: 99%

Active scissor-jack for seismic risk mitigation of building structures

2019

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“…Despite artificial neural network and GA, which have been studied in some of the previous works in active structural control area, RL has not been paid attention to a satisfactory level for the purpose of parameters learning despite having distinctive features. For instance, trained offline artificial neural network controllers and cerebellar model articulation controllers are very popular methods in the structural control …”

Section: Control Designmentioning

confidence: 99%

“…For instance, trained offline artificial neural network controllers and cerebellar model articulation controllers are very popular methods in the structural control. [44][45][46][47][48][49][50] RL algorithm deals with the problems in which an independent agent comprehends some states and performs some tasks to reach its goal according to its perception. Each time the agent conducts an action in the environment, it receives a reward or punishment according to the action and state.…”

Section: Control Designmentioning

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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“…Because this approach does not require accurate mathematical models of plants, model-free controller design can be established [28]- [31]. For example, vibration control of a building structure using a PI/PID controller combined with a fuzzy controller to compensate for the nonlinear restoring force was proposed [32]. Fuzzy controller design lacks systematic approaches to set control rules and membership functions.…”

Section: Introductionmentioning

confidence: 99%