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Готові списки джерел за темами / Active structures

Добірка наукової літератури з теми "Active structures"

Автор: Grafiati

Опубліковано: 7 липня 2024

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Статті в журналах з теми "Active structures":

1

Soong, T. T., and G. D. Manolis. "Active Structures." Journal of Structural Engineering 113, no. 11 (November 1987): 2290–302. http://dx.doi.org/10.1061/(asce)0733-9445(1987)113:11(2290).

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2

Pantelides, C. P., and S. R. Tzan. "Active structures with uncertainties." International Journal of Computer Applications in Technology 13, no. 1/2 (2000): 59. http://dx.doi.org/10.1504/ijcat.2000.000224.

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3

D'Isep, F., and L. Sertorio. "Irreversibility for active structures." Il Nuovo Cimento B 94, no. 2 (August 1986): 168–74. http://dx.doi.org/10.1007/bf02759755.

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4

Soong, T. T., and B. F. Spencer. "Active, semi-active and hybrid control of structures." Bulletin of the New Zealand Society for Earthquake Engineering 33, no. 3 (September 30, 2000): 387–402. http://dx.doi.org/10.5459/bnzsee.33.3.387-402.

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In recent years, considerable attention has been paid to research and development of passive and active structural control devices, with particular emphasis on alleviation of wind and seismic response of buildings and bridges. In both areas, serious efforts have been undertaken to develop the structural control concept into a workable technology, and today we have many such devices installed in a wide variety of structures. The focus of this state-of-the-art paper is on active, semi-active and hybrid structural control with seismic applications. These systems employ controllable force devices integrated with sensors, controllers and real-time information processing. This paper includes a brief historical outline of their development and an assessment of the state-of-the-art and state-of-the-practice of this exciting, and still evolving, technology. Also included in the discussion are their advantages and limitations in the context of seismic design and retrofit of civil engineering structures.

5

Qureshi, Sohail M., Hajime Tsutsumi, Kiyoshi Uno, and Shoichi Kitagawa. "ACTIVE CONTROL OF SLIDING STRUCTURES." PROCEEDINGS OF THE JSCE EARTHQUAKE ENGINEERING SYMPOSIUM 21 (1991): 493–96. http://dx.doi.org/10.2208/proee1957.21.493.

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6

Chang, C. M., B. M. Al-Hashimi, and J. N. Ross. "Unified active filter biquad structures." IEE Proceedings - Circuits, Devices and Systems 151, no. 4 (2004): 273. http://dx.doi.org/10.1049/ip-cds:20040132.

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7

Pearl, Laurence. "Similarity of active-site structures." Nature 362, no. 6415 (March 1993): 24. http://dx.doi.org/10.1038/362024a0.

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8

Nathal, Michael V., and George L. Stefko. "Smart Materials and Active Structures." Journal of Aerospace Engineering 26, no. 2 (April 2013): 491–99. http://dx.doi.org/10.1061/(asce)as.1943-5525.0000319.

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9

Sirlin, S., C. Paliou, R. W. Longman, M. Shinozuka, and E. Samaras. "Active Control of Floating Structures." Journal of Engineering Mechanics 112, no. 9 (September 1986): 947–65. http://dx.doi.org/10.1061/(asce)0733-9399(1986)112:9(947).

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10

Reinhorn, A. M., G. D. Manolis, and C. Y. Wen. "Active Control of Inelastic Structures." Journal of Engineering Mechanics 113, no. 3 (March 1987): 315–33. http://dx.doi.org/10.1061/(asce)0733-9399(1987)113:3(315).

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Дисертації з теми "Active structures":

1

Toews, von Riesen Eduard. "Active hyperhelical structures." Thesis, University of Cambridge, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.612458.

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2

Keyhani, Ali. "A Study On The Predictive Optimal Active Control Of Civil Engineering Structures." Thesis, Indian Institute of Science, 2000. https://etd.iisc.ac.in/handle/2005/223.

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Uncertainty involved in the safe and comfort design of the structures is a major concern of civil engineers. Traditionally, the uncertainty has been overcome by utilizing various and relatively large safety factors for loads and structural properties. As a result in conventional design of for example tall buildings, the designed structural elements have unnecessary dimensions that sometimes are more than double of the ones needed to resist normal loads. On the other hand the requirements for strength and safety and comfort can be conflicting. Consequently, an alternative approach for design of the structures may be of great interest in design of safe and comfort structures that also offers economical advantages. Recently, there has been growing interest among the researchers in the concept of structural control as an alternative or complementary approach to the existing approaches of structural design. A few buildings have been designed and built based on this concept. The concept is to utilize a device for applying a force (known as control force) to encounter the effects of disturbing forces like earthquake force. However, the concept still has not found its rightful place among the practical engineers and more research is needed on the subject. One of the main problems in structural control is to find a proper algorithm for determining the optimum control force that should be applied to the structure. The investigation reported in this thesis is concerned with the application of active control to civil engineering structures. From the literature on control theory. (Particularly literature on the control of civil engineering structures) problems faced in application of control theory were identified and classified into two categories: 1) problems common to control of all dynamical systems, and 2) problems which are specially important in control of civil engineering structures. It was concluded that while many control algorithms are suitable for control of dynamical systems, considering the special problems in controlling civil structures and considering the unique future of structural control, many otherwise useful control algorithms face practical problems in application to civil structures. Consequently a set of criteria were set for judging the suitability of the control algorithms for use in control of civil engineering structures. Various types of existing control algorithms were investigated and finally it was concluded that predictive optimal control algorithms possess good characteristics for purpose of control of civil engineering structures. Among predictive control algorithms, those that use ARMA stochastic models for predicting the ground acceleration are better fitted to the structural control environment because all the past measured excitation is used to estimate the trends of the excitation for making qualified guesses about its coming values. However, existing ARMA based predictive algorithms are devised specially for earthquake and require on-line measurement of the external disturbing load which is not possible for dynamic loads like wind or blast. So, the algorithms are not suitable for tall buildings that experience both earthquake and wind loads during their life. Consequently, it was decided to establish a new closed loop predictive optimal control based on ARMA models as the first phase of the study. In this phase it was initially established that ARMA models are capable of predicting response of a linear SDOF system to the earthquake excitation a few steps ahead. The results of the predictions encouraged a search for finding a new closed loop optimal predictive control algorithm for linear SDOF structures based on prediction of the response by ARMA models. The second part of phase I, was devoted to developing and testing the proposed algorithm The new developed algorithm is different from other ARMA based optimal controls since it uses ARMA models for prediction of the structure response while existing algorithms predict the input excitation. Modeling the structure response as an AR or ARMA stochastic process is an effective mean for prediction of the structure response while avoiding measurement of the input excitation. ARMA models used in the algorithm enables it to avoid or reduce the time delay effect by predicting the structure response a few steps ahead. Being a closed loop control, the algorithm is suitable for all structural control conditions and can be used in a single control mechanism for vibration control of tall buildings against wind, earthquake or other random dynamic loads. Consequently the standby time is less than that for existing ARMA based algorithms devised only for earthquakes. This makes the control mechanism more reliable. The proposed algorithm utilizes and combines two different mathematical models. First model is an ARMA model representing the environment and the structure as a single system subjected to the unknown random excitation and the second model is a linear SDOF system which represents the structure subjected to a known past history of the applied control force only. The principle of superposition is then used to combine the results of these two models to predict the total response of the structure as a function of the control force. By using the predicted responses, the minimization of the performance index with respect to the control force is carried out for finding the optimal control force. As phase II, the proposed predictive control algorithm was extended to structures that are more complicated than linear SDOF structures. Initially, the algorithm was extended to linear MDOF structures. Although, the development of the algorithm for MDOF structures was relatively straightforward, during testing of the algorithm, it was found that prediction of the response by ARMA models can not be done as was done for SDOF case. In the SDOF case each of the two components of the state vector (i.e. displacement and velocity) was treated separately as an ARMA stochastic process. However, applying the same approach to each component of the state vector of a MDOF structure did not yield satisfactory results in prediction of the response. Considering the whole state vector as a multi-variable ARMA stochastic vector process yielded the desired results in predicting the response a few steps ahead. In the second part of this phase, the algorithm was extended to non-linear MDOF structures. Since the algorithm had been developed based on the principle of superposition, it was not possible to directly extend the algorithm to non-linear systems. Instead, some generalized response was defined. Then credibility of the ARMA models in predicting the generalized response was verified. Based on this credibility, the algorithm was extended for non-linear MDOF structures. Also in phase II, the stability of a controlled MDOF structure was proved. Both internal and external stability of the system were described and verified. In phase III, some problems of special interest, i.e. soil-structure interaction and control time delay, were investigated and compensated for in the framework of the developed predictive optimal control. In first part of phase III soil-structure interaction was studied. The half-space solution of the SSI effect leads to a frequency dependent representation of the structure-footing system, which is not fit for control purpose. Consequently an equivalent frequency independent system was proposed and defined as a system whose frequency response is equal to the original structure -footing system in the mean squares sense. This equivalent frequency independent system then was used in the control algorithm. In the second part of this phase, an analytical approach was used to tackle the time delay phenomenon in the context of the predictive algorithm described in previous chapters. A generalized performance index was defined considering time delay. Minimization of the generalized performance index resulted into a modified version of the algorithm in which time delay is compensated explicitly. Unlike the time delay compensation technique used in the previous phases of this investigation, which restricts time delay to be an integer multiplier of the sampling period, the modified algorithm allows time delay to be any non-negative number. However, the two approaches produce the same results if time delay is an integer multiplier of the sampling period. For evaluating the proposed algorithm and comparing it with other algorithms, several numerical simulations were carried during the research by using MATLAB and its toolboxes. A few interesting results of these simulations are enumerated below: ARM A models are able to predict the response of both linear and non-linear structures to random inputs such as earthquakes. The proposed predictive optimal control based on ARMA models has produced better results in the context of reducing velocity, displacement, total energy and operational cost compared to classic optimal control. Proposed active control algorithm is very effective in increasing safety and comfort. Its performance is not affected much by errors in the estimation of system parameters (e.g. damping). The effect of soil-structure interaction on the response to control force is considerable. Ignoring SSI will cause a significant change in the magnitude of the frequency response and a shift in the frequencies of the maximum response (resonant frequencies). Compensating the time delay effect by the modified version of the proposed algorithm will improve the performance of the control system in achieving the control goal and reduction of the structural response.

3

Keyhani, Ali. "A Study On The Predictive Optimal Active Control Of Civil Engineering Structures." Thesis, Indian Institute of Science, 2000. http://hdl.handle.net/2005/223.

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Анотація:

Uncertainty involved in the safe and comfort design of the structures is a major concern of civil engineers. Traditionally, the uncertainty has been overcome by utilizing various and relatively large safety factors for loads and structural properties. As a result in conventional design of for example tall buildings, the designed structural elements have unnecessary dimensions that sometimes are more than double of the ones needed to resist normal loads. On the other hand the requirements for strength and safety and comfort can be conflicting. Consequently, an alternative approach for design of the structures may be of great interest in design of safe and comfort structures that also offers economical advantages. Recently, there has been growing interest among the researchers in the concept of structural control as an alternative or complementary approach to the existing approaches of structural design. A few buildings have been designed and built based on this concept. The concept is to utilize a device for applying a force (known as control force) to encounter the effects of disturbing forces like earthquake force. However, the concept still has not found its rightful place among the practical engineers and more research is needed on the subject. One of the main problems in structural control is to find a proper algorithm for determining the optimum control force that should be applied to the structure. The investigation reported in this thesis is concerned with the application of active control to civil engineering structures. From the literature on control theory. (Particularly literature on the control of civil engineering structures) problems faced in application of control theory were identified and classified into two categories: 1) problems common to control of all dynamical systems, and 2) problems which are specially important in control of civil engineering structures. It was concluded that while many control algorithms are suitable for control of dynamical systems, considering the special problems in controlling civil structures and considering the unique future of structural control, many otherwise useful control algorithms face practical problems in application to civil structures. Consequently a set of criteria were set for judging the suitability of the control algorithms for use in control of civil engineering structures. Various types of existing control algorithms were investigated and finally it was concluded that predictive optimal control algorithms possess good characteristics for purpose of control of civil engineering structures. Among predictive control algorithms, those that use ARMA stochastic models for predicting the ground acceleration are better fitted to the structural control environment because all the past measured excitation is used to estimate the trends of the excitation for making qualified guesses about its coming values. However, existing ARMA based predictive algorithms are devised specially for earthquake and require on-line measurement of the external disturbing load which is not possible for dynamic loads like wind or blast. So, the algorithms are not suitable for tall buildings that experience both earthquake and wind loads during their life. Consequently, it was decided to establish a new closed loop predictive optimal control based on ARMA models as the first phase of the study. In this phase it was initially established that ARMA models are capable of predicting response of a linear SDOF system to the earthquake excitation a few steps ahead. The results of the predictions encouraged a search for finding a new closed loop optimal predictive control algorithm for linear SDOF structures based on prediction of the response by ARMA models. The second part of phase I, was devoted to developing and testing the proposed algorithm The new developed algorithm is different from other ARMA based optimal controls since it uses ARMA models for prediction of the structure response while existing algorithms predict the input excitation. Modeling the structure response as an AR or ARMA stochastic process is an effective mean for prediction of the structure response while avoiding measurement of the input excitation. ARMA models used in the algorithm enables it to avoid or reduce the time delay effect by predicting the structure response a few steps ahead. Being a closed loop control, the algorithm is suitable for all structural control conditions and can be used in a single control mechanism for vibration control of tall buildings against wind, earthquake or other random dynamic loads. Consequently the standby time is less than that for existing ARMA based algorithms devised only for earthquakes. This makes the control mechanism more reliable. The proposed algorithm utilizes and combines two different mathematical models. First model is an ARMA model representing the environment and the structure as a single system subjected to the unknown random excitation and the second model is a linear SDOF system which represents the structure subjected to a known past history of the applied control force only. The principle of superposition is then used to combine the results of these two models to predict the total response of the structure as a function of the control force. By using the predicted responses, the minimization of the performance index with respect to the control force is carried out for finding the optimal control force. As phase II, the proposed predictive control algorithm was extended to structures that are more complicated than linear SDOF structures. Initially, the algorithm was extended to linear MDOF structures. Although, the development of the algorithm for MDOF structures was relatively straightforward, during testing of the algorithm, it was found that prediction of the response by ARMA models can not be done as was done for SDOF case. In the SDOF case each of the two components of the state vector (i.e. displacement and velocity) was treated separately as an ARMA stochastic process. However, applying the same approach to each component of the state vector of a MDOF structure did not yield satisfactory results in prediction of the response. Considering the whole state vector as a multi-variable ARMA stochastic vector process yielded the desired results in predicting the response a few steps ahead. In the second part of this phase, the algorithm was extended to non-linear MDOF structures. Since the algorithm had been developed based on the principle of superposition, it was not possible to directly extend the algorithm to non-linear systems. Instead, some generalized response was defined. Then credibility of the ARMA models in predicting the generalized response was verified. Based on this credibility, the algorithm was extended for non-linear MDOF structures. Also in phase II, the stability of a controlled MDOF structure was proved. Both internal and external stability of the system were described and verified. In phase III, some problems of special interest, i.e. soil-structure interaction and control time delay, were investigated and compensated for in the framework of the developed predictive optimal control. In first part of phase III soil-structure interaction was studied. The half-space solution of the SSI effect leads to a frequency dependent representation of the structure-footing system, which is not fit for control purpose. Consequently an equivalent frequency independent system was proposed and defined as a system whose frequency response is equal to the original structure -footing system in the mean squares sense. This equivalent frequency independent system then was used in the control algorithm. In the second part of this phase, an analytical approach was used to tackle the time delay phenomenon in the context of the predictive algorithm described in previous chapters. A generalized performance index was defined considering time delay. Minimization of the generalized performance index resulted into a modified version of the algorithm in which time delay is compensated explicitly. Unlike the time delay compensation technique used in the previous phases of this investigation, which restricts time delay to be an integer multiplier of the sampling period, the modified algorithm allows time delay to be any non-negative number. However, the two approaches produce the same results if time delay is an integer multiplier of the sampling period. For evaluating the proposed algorithm and comparing it with other algorithms, several numerical simulations were carried during the research by using MATLAB and its toolboxes. A few interesting results of these simulations are enumerated below: ARM A models are able to predict the response of both linear and non-linear structures to random inputs such as earthquakes. The proposed predictive optimal control based on ARMA models has produced better results in the context of reducing velocity, displacement, total energy and operational cost compared to classic optimal control. Proposed active control algorithm is very effective in increasing safety and comfort. Its performance is not affected much by errors in the estimation of system parameters (e.g. damping). The effect of soil-structure interaction on the response to control force is considerable. Ignoring SSI will cause a significant change in the magnitude of the frequency response and a shift in the frequencies of the maximum response (resonant frequencies). Compensating the time delay effect by the modified version of the proposed algorithm will improve the performance of the control system in achieving the control goal and reduction of the structural response.

4

Ulker, Fatma Demet. "Active Vibration Control Of Smart Structures." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/4/1098409/index.pdf.

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The purpose of this thesis was to design controllers by using H1 and ¹
control strategies in order to suppress the free and forced vibrations of smart structures. The smart structures analyzed in this study were the smart beam and the smart ¯
n. They were aluminum passive structures with surface bonded PZT (Lead-Zirconate-Titanate) patches. The structures were considered in clamped-free con¯
guration. The ¯
rst part of this study focused on the identi¯
cation of nominal system models of the smart structures from the experimental data. For the experimentally identi¯
ed models the robust controllers were designed by using H1 and ¹
-synthesis strategies. In the second part, the controller implementation was carried out for the suppression of free and forced vibrations of the smart structures. Within the framework of this study, a Smart Structures Laboratory was established in the Aerospace Engineering Department of METU. The controller implementations were carried out by considering two di®
erent experimental set-ups. In the ¯
rst set-up the controller designs were based on the strain measurements. In the second approach, the displacement measurements, which were acquired through laser displacement sensor, were considered in the controller design. The ¯
rst two °
exural modes of the smart beam were successfully controlled by using H1 method. The vibrations of the ¯
rst two °
exural and ¯
rst torsional modes of the smart ¯
n were suppressed through the ¹
-synthesis. Satisfactory attenuation levels were achieved for both strain measurement and displacement measurement applications.

5

Chang, Min-Yung. "Active vibration control of composite structures." Diss., This resource online, 1990. http://scholar.lib.vt.edu/theses/available/etd-09162005-115021/.

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6

Pennington, Philip Nigel. "Active integrated optic waveguide/laser structures." Thesis, University of Bath, 1989. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.760599.

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7

Maldonado-Mercado, Julio Cesar. "Passive and active control of structures." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/36654.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1995.
Includes bibliographical references (leaves 94-98).
by Julio Cesar Maldonado-Mercado.
M.S.

8

Tsai, Frank J. (Frank Jin-Fong) 1976. "Distributed active control for tension structures." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/81544.

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9

Scruggs, Jeffrey. "Active, Regenerative Control of Civil Structures." Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/34332.

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An analysis is presented on the use of a proof-mass actuator as a regenerative force actuator for the mitigation of earthquake disturbances in civil structures. A proof-mass actuator is a machine which accelerates a mass along a linear path. Such actuators can facilitate two-way power flow. In regenerative force actuation, a bi- directional power-electronic drive is used to facilitate power flow both to and from the proof-mass actuator power supply. With proper control system design, this makes it possible to suppress a disturbance on a structure using mostly energy extracted from the disturbance itself, rather than from an external power source. In this study, three main objectives are accomplished. First, a new performance measure, called the "required energy capacity," is proposed as an assessment of the minimum size of the electric power supply necessary to facilitate the power flow required of the closed-loop system for a given disturbance. The relationship between the required energy capacity and the linear control system design, which is based on positive position feedback concepts, is developed. The dependency of the required energy capacity on hybrid realizations of the control law are discussed, and hybrid designs are found which minimize this quantity for specific disturbance characteristics. As the second objective, system identification and robust estimation methods are used to develop a stochastic approach to the performance assessment of structural control systems, which evaluates the average worst-case performance for all earthquakes "similar" to an actual data record. This technique is used to evaluate the required energy capacity for a control system design. In the third objective, a way is found to design a battery capacity which takes into account the velocity rating of the proof-mass actuator. Upon sizing this battery, two nonlinear controllers are proposed which automatically regulate the power flow in the closed-loop system to accommodate a power supply with a finite energy capacity, regardless of the disturbance size. Both controllers are based on a linear control system design. One includes a nonlinearity which limits power flow out of the battery supply. The other includes a nonlinearity which limits the magnitude of the proof-mass velocity. The latter of these is shown to yield superior performance.
Master of Science

10

Maillard, Julien. "Advanced Time Domain Sensing For Active Structural Acoustic Control." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/30335.

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Active control of sound radiation from vibrating structures has been an area of much research in the past decade. In Active Structural Acoustic Control (ASAC), the minimization of sound radiation is achieved by modifying the response of the structure through structural inputs rather than by exciting the acoustic medium (Active Noise Control, ANC). The ASAC technique often produces global far-field sound attenuation with relatively few actuators as compared to ANC. The structural control inputs of ASAC systems are usually constructed adaptively in the time domain based on a number of error signals to be minimized. One of the primary concerns in active control of sound is then to provide the controller with appropriate ``error'' information. Early investigations have implemented far-field microphones, thereby providing the controller with actual radiated pressure information. Most structure-borne sound control approaches now tend to eliminate the use of microphones by developing sensors that are integrated in the structure. This study presents a new sensing technique implementing such an approach. A structural acoustic sensor is developed for estimating radiation information from vibrating structures. This technique referred to as Discrete Structural Acoustic Sensing (DSAS) provides time domain estimates of the radiated sound pressure at prescribed locations in the far field over a broad frequency range. The structural acoustic sensor consists of a set of accelerometers mounted on the radiating structure and arrays of digital filters that process the measured acceleration signals in real time. The impulse response of each filter is constructed from the appropriate radiation Green's function for the source area associated with each accelerometer. Validation of the sensing technique is performed on two different systems: a baffled rectangular plate and a baffled finite cylinder. For both systems, the sensor is first analyzed in terms of prediction accuracy by comparing estimated and actual sound pressure radiated in the far field. The analysis is carried out on a numerical model of the plate and cylinder as well as on the real structures through experimental testing. The sensor is then implemented in a broadband radiation control system. The plate and cylinder are excited by broadband disturbance inputs over a frequency range encompassing several of the first flexural resonances of the structure. Single-sided piezo-electric actuators provide the structural control inputs while the sensor estimates are used as error signals. The controller is based on the filtered-x version of the adaptive LMS algorithm. Results from both analytical and experimental investigations are again presented for the two systems. Additional control results based on error microphones allow a comparison of the two sensing approaches in terms of control performance. The major outcome of this study is the ability of the structural acoustic sensor to effectively replace error microphones in broadband radiation control systems. In particular, both analytical and experimental results show the level of sound attenuation achieved when implementing Discrete Structural Acoustic Sensing rivaled that achieved with far-field error microphones. Finally, the approach presents a significant alternative over other existing structural sensing techniques as it requires very little system modeling.
Ph. D.

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Книги з теми "Active structures":

1

Preumont, André. Active control of structures. Chichester, United Kingdom: John Wiley, 2008.

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2

Gawronski, Wodek K., ed. Advanced Structural Dynamics and Active Control of Structures. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-0-387-72133-0.

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3

Preumont, André. Vibration Control of Active Structures. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5654-7.

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4

Cavallo, Alberto, Giuseppe De Maria, Ciro Natale, and Salvatore Pirozzi. Active Control of Flexible Structures. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-281-0.

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5

Preumont, A. Vibration Control of Active Structures. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-2033-6.

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6

Preumont, André. Vibration Control of Active Structures. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72296-2.

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7

Preumont, André. Vibration control of active structures: An introduction. 2nd ed. Dordrecht: Kluwer Academic Publishers, 2002.

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8

Preumont, André. Vibration control of active structures: An introduction. 3rd ed. Berlin: Springer, 2011.

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9

Preumont, André. Vibration control of active structures: An introduction. 2nd ed. Dordrecht: Kluwer Academic Publishers, 2002.

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10

Preumont, André. Vibration control of active structures: An introduction. Dordrecht: Kluwer Academic Publishers, 1997.

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Частини книг з теми "Active structures":

1

Peraza Hernandez, Edwin A., Darren J. Hartl, and Dimitris C. Lagoudas. "Structural Mechanics and Design of Active Origami Structures." In Active Origami, 331–409. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-91866-2_8.

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2

Peraza Hernandez, Edwin A., Darren J. Hartl, and Dimitris C. Lagoudas. "Introduction to Active Origami Structures." In Active Origami, 1–53. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-91866-2_1.

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3

Soong, T. T., and H. Gupta. "Active Structural Control Against Wind." In Smart Structures, 329–36. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4611-1_37.

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4

Preumont, André. "Active Control of Large Telescopes: Active Optics." In Vibration Control of Active Structures, 449–68. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72296-2_17.

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5

Magaña, M. E., J. Rodellar, J. R. Casas, and J. Mas. "Active Control of Cable-Stayed Bridges." In Smart Structures, 193–202. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4611-1_22.

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6

Peraza Hernandez, Edwin A., Darren J. Hartl, and Dimitris C. Lagoudas. "Kinematics of Origami Structures with Creased Folds." In Active Origami, 55–110. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-91866-2_2.

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7

Peraza Hernandez, Edwin A., Darren J. Hartl, and Dimitris C. Lagoudas. "Kinematics of Origami Structures with Smooth Folds." In Active Origami, 201–68. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-91866-2_5.

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8

Sinapius, Johannes Michael, Christian Hühne, Hossein Sadri, and Johannes Riemenschneider. "Active Shape Control." In Adaptronics – Smart Structures and Materials, 155–225. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-61399-3_5.

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9

Michael Sinapius, Johannes, Björn Timo Kletz, and Steffen Opitz. "Active Vibration Control." In Adaptronics – Smart Structures and Materials, 227–329. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-61399-3_6.

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Sinapius, Johannes Michael, and Malte Misol. "Active Sound Control." In Adaptronics – Smart Structures and Materials, 355–424. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-61399-3_8.

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Тези доповідей конференцій з теми "Active structures":

1

Lane, Jeffrey, and Aldo Ferri. "Control of a flexible structure using combined active and semi-active element." In 36th Structures, Structural Dynamics and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1995. http://dx.doi.org/10.2514/6.1995-1236.

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2

Charon, W. "Structural design of active precision structures." In First European Conference on Smart Structures and Materials. SPIE, 1992. http://dx.doi.org/10.1117/12.2298094.

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3

FANSON, J., G. BLACKWOOD, and C. CHU. "Active-member control of precision structures." In 30th Structures, Structural Dynamics and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1989. http://dx.doi.org/10.2514/6.1989-1329.

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4

Steadman, D., S. Hanagud, and S. Atluri. "Experiments towards active delamination control." In 36th Structures, Structural Dynamics and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1995. http://dx.doi.org/10.2514/6.1995-1385.

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5

AGNES, GREGORY, and KEVIN NAPOLITANO. "ACTIVE CONSTRAINED LAYER VISCOELASTIC DAMPING." In 34th Structures, Structural Dynamics and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1993. http://dx.doi.org/10.2514/6.1993-1702.

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6

LURIE, B., J. FANSON, and R. LASKIN. "Active suspensions for vibration isolation." In 32nd Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1991. http://dx.doi.org/10.2514/6.1991-1232.

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7

Cuerda, Javier, Felix Ruting, Francisco J. Garcia-Vidal, and Jorge Bravo-Abad. "Lasing action in active plasmonic structures." In 2015 9th International Congress on Advanced Electromagnetic Materials in Microwaves and Optics (METAMATERIALS). IEEE, 2015. http://dx.doi.org/10.1109/metamaterials.2015.7342449.

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8

Antonakos, Epameinondas, Joan Alabort-i-Medina, and Stefanos Zafeiriou. "Active Pictorial Structures." In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015. http://dx.doi.org/10.1109/cvpr.2015.7299182.

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9

Cesnik, Carlos, and Miguel Ortega-Morales. "Active composite beam cross-sectional modeling - Stiffness and active force constants." In 40th Structures, Structural Dynamics, and Materials Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1999. http://dx.doi.org/10.2514/6.1999-1548.

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10

CHEN, JAY-CHUNG, and JAMES FANSON. "System identification test using active members." In 30th Structures, Structural Dynamics and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1989. http://dx.doi.org/10.2514/6.1989-1290.

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Звіти організацій з теми "Active structures":

1

Fuller, Chris R. Active Structural Acoustic Control and Smart Structures. Fort Belvoir, VA: Defense Technical Information Center, September 1991. http://dx.doi.org/10.21236/ada248341.

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2

Farrar, C., W. Baker, J. Fales, and D. Shevitz. Active vibration control of civil structures. Office of Scientific and Technical Information (OSTI), November 1996. http://dx.doi.org/10.2172/400183.

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3

Shiroma, Wayne A., and Jung-Chih Chiao. Active and Reconfigurable Photonic-Bandgap Structures. Fort Belvoir, VA: Defense Technical Information Center, March 2002. http://dx.doi.org/10.21236/ada411049.

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4

Wilks, Yorick, Michael Coombs, Roger T. Hartley, and Dihong Qiu. Active Knowledge Structures for Natural Language Processing. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada245893.

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5

Brei, Diann, Jonathan Luntz, and Julianna Abel. Active Knits for Radical Change Air Force Structures. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada579083.

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6

Noble, Richard D., and Douglas L. Gin. Novel Nanocomposite Structures as Active and Passive Barrier Materials. Fort Belvoir, VA: Defense Technical Information Center, June 2010. http://dx.doi.org/10.21236/ada533484.

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7

Sadek, Fahim, and Bijan Mohraz. Semi-active control algorithms for structures with variable dampers. Gaithersburg, MD: National Institute of Standards and Technology, 1997. http://dx.doi.org/10.6028/nist.ir.6052.

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8

Goldner, R. B. Attaining a solar energy economy with active thin film structures. Office of Scientific and Technical Information (OSTI), October 1995. http://dx.doi.org/10.2172/132828.

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9

Baumann, William T., Richard L. Moose, Hugh F. VanLandigham, Mauro J. Caputi, Stephen H. Jones, and Bhaskar Gorti. Active Control of Generalized Complex Modal Structures in a Stochastic Environment. Fort Belvoir, VA: Defense Technical Information Center, May 1992. http://dx.doi.org/10.21236/ada251910.

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10

Wang, Kon-Well. Active-Passive Hybrid Adaptive Structures for Vibration Controls -- An Integrated Approach. Fort Belvoir, VA: Defense Technical Information Center, April 2000. http://dx.doi.org/10.21236/ada384416.

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