A LQR Controller Design for Active Vibration Control of Flexible Structures

Zhang, Jingjun; He, Longzhuang; Wang, Ercheng; Gao, Ruizhen

doi:10.1109/paciia.2008.358

Cited by 25 publications

(15 citation statements)

References 11 publications

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“…Modeling of piezoelectric smart structures by the finite element method has been presented [8,12,15]. The global matrix equations governing a smart structure system can be written as: …”

Section: Finite Element Modeling Of the Smart Platementioning

confidence: 99%

The LQR Control Active of Smart Plate Based on the Finite Element Method

Latrache

Amrane

2017

Period. Polytech. Mech. Eng.

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“…Modeling of piezoelectric smart structures by the finite element method has been presented [8,12,15]. The global matrix equations governing a smart structure system can be written as: …”

Section: Finite Element Modeling Of the Smart Platementioning

confidence: 99%

The LQR Control Active of Smart Plate Based on the Finite Element Method

Latrache

Amrane

2017

Period. Polytech. Mech. Eng.

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“…LQR optimal control can make the original system achieve better performance index with cheap control effort. Some other studies have illustrated different controllers for active vibration control purpose [16][17][18][19][20][21]; however, these two mentioned algorithms have not been widely used in wind tunnel tests yet. In order to introduce new applications of the advanced algorithms, this paper presents the details of the design of PD, NNPID, and LQR controllers.…”

Section: Introductionmentioning

confidence: 99%

Active Vibration Control of the Sting Used in Wind Tunnel: Comparison of Three Control Algorithms

Shen

Dai

Chen

et al. 2018

Shock and Vibration

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In wind tunnel tests, cantilever stings are often used as model-mount in order to reduce flow interference on experimental data. In this case, however, large-amplitude vibration and low-frequency vibration are easily produced on the system, which indicates the potential hazards of gaining inaccurate data and even damaging the structure. is paper details three algorithms, respectively, Classical PD Algorithm, Artificial Neural Network PID (NNPID), and Linear Quadratic Regulator (LQR) Optimal Control Algorithm, which can realize active vibration control of sting used in wind tunnel. e hardware platform of the first-order vibration damping system based on piezoelectric structure is set up and the real-time control software is designed to verify the feasibility and practicability of the algorithms. While the PD algorithm is the most common method in engineering, the results show that all the algorithms can achieve the purpose of over 80% reduction, and the last two algorithms perform even better. Besides, self-tuning is realized in NNPID, and with the help of the Observer/Kalman Filter Identification (OKID), LQR optimal control algorithm can make the control effort as small as possible. e paper proves the superiority of NNPID and LQR algorithms and can be an available reference for vibration control of wind tunnel system.

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“…Generally, these methods can be divided in two groups. The first group includes control methods which require a mathematical model of the system to operate (Gholampour et al 2014, Suhir 2014, Zhang et al 2008. Although structural models can be developed, there are many sources of uncertainty, measurement noise and nonlinearity that result in less-effective control algorithms.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear control of structure using neuro-predictive algorithm

Baghban¹,

Karamodin²,

Haji-Kazemi³

2015

Smart Structures and Systems

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A new neural network (NN) predictive controller (NNPC) algorithm has been developed and tested in the computer simulation of active control of a nonlinear structure. In the present method an NN is used as a predictor. This NN has been trained to predict the future response of the structure to determine the control forces. These control forces are calculated by minimizing the difference between the predicted and desired responses via a numerical minimization algorithm. Since the NNPC is very time consuming and not suitable for real-time control, it is then used to train an NN controller. To consider the effectiveness of the controller on probability of damage, fragility curves are generated. The approach is validated by using simulated response of a 3 story nonlinear benchmark building excited by several historical earthquake records. The simulation results are then compared with a linear quadratic Gaussian (LQG) active controller. The results indicate that the proposed algorithm is completely effective in relative displacement reduction.

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A LQR Controller Design for Active Vibration Control of Flexible Structures

Cited by 25 publications

References 11 publications

The LQR Control Active of Smart Plate Based on the Finite Element Method

The LQR Control Active of Smart Plate Based on the Finite Element Method

Active Vibration Control of the Sting Used in Wind Tunnel: Comparison of Three Control Algorithms

Nonlinear control of structure using neuro-predictive algorithm

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