Статті в журналах з теми "Sensors and actuators placement"

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1

CHEN, KEVIN K., and CLARENCE W. ROWLEY. "H2 optimal actuator and sensor placement in the linearised complex Ginzburg–Landau system." Journal of Fluid Mechanics 681 (June 20, 2011): 241–60. http://dx.doi.org/10.1017/jfm.2011.195.

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The linearised complex Ginzburg–Landau equation is a model for the evolution of small fluid perturbations, such as in a bluff body wake. By implementing actuators and sensors and designing an H2 optimal controller, we control a supercritical, infinite-domain formulation of this system. We seek the optimal actuator and sensor placement that minimises the H2 norm of the controlled system, from flow disturbances and sensor noise to a cost on the perturbation and input magnitudes. We formulate the gradient of the H2 squared norm with respect to the actuator and sensor placements and iterate towards the optimal placement. When stochastic flow disturbances are present everywhere in the spatial domain, it is optimal to place the actuator just upstream of the origin and the sensor just downstream. With pairs of actuators and sensors, it is optimal to place each actuator slightly upstream of each corresponding sensor, and scatter the pairs throughout the spatial domain. When disturbances are only introduced upstream, the optimal placement shifts upstream as well. Global mode and Gramian analyses fail to predict the optimal placement; they produce H2 norms about five times higher than at the true optimum. The wavemaker region is a better guess for the optimal placement.
2

Šolek, Peter, and Marek Maták. "An Active Control of the Thin-Walled Mechanical Systems." Applied Mechanics and Materials 611 (August 2014): 22–31. http://dx.doi.org/10.4028/www.scientific.net/amm.611.22.

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This article deals with the influence of optimal actuator and sensor placement on the active control of thin-walled mechanical systems. The approach used for optimal actuator and sensor placement is based on the evaluation norms and. The optimal actuator and sensor placement satisfied the requirements on the controllability, observability and spillover prevention. The investigation of the optimal placement of actuators and sensors is demonstrated on the active vibration of the thin-walled two dimensional mechanical systems.
3

Mersch, Johannes, Najmeh Keshtkar, Henriette Grellmann, Carlos Alberto Gomez Cuaran, Mathis Bruns, Andreas Nocke, Chokri Cherif, Klaus Röbenack, and Gerald Gerlach. "Integrated Temperature and Position Sensors in a Shape-Memory Driven Soft Actuator for Closed-Loop Control." Materials 15, no. 2 (January 10, 2022): 520. http://dx.doi.org/10.3390/ma15020520.

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Soft actuators are a promising option for the advancing fields of human-machine interaction and dexterous robots in complex environments. Shape memory alloy wire actuators can be integrated into fiber rubber composites for highly deformable structures. For autonomous, closed-loop control of such systems, additional integrated sensors are necessary. In this work, a soft actuator is presented that incorporates fiber-based actuators and sensors to monitor both deformation and temperature. The soft actuator showed considerable deformation around two solid body joints, which was then compared to the sensor signals, and their correlation was analyzed. Both, the actuator as well as the sensor materials were processed by braiding and tailored fiber placement before molding with silicone rubber. Finally, the novel fiber-rubber composite material was used to implement closed-loop control of the actuator with a maximum error of 0.5°.
4

Seyed Sakha, Masoud, and Hamid Reza Shaker. "Optimal sensors and actuators placement for large-scale unstable systems via restricted genetic algorithm." Engineering Computations 34, no. 8 (November 6, 2017): 2582–97. http://dx.doi.org/10.1108/ec-04-2016-0138.

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Purpose One of the fundamental problems in control systems engineering is the problem of sensors and actuators placement. Decisions in this context play a key role in the success of control process. The methods developed for optimal placement of the sensors and actuators are known to be computationally expensive. The computational burden is significant, in particular, for large-scale systems. The purpose of this paper is to improve and extend the state-of-the-art methods within this field. Design/methodology/approach In this paper, a new technique is developed for placing sensor and actuator in large-scale systems by using restricted genetic algorithm (RGA). RGA is a kind of genetic algorithm which is developed specifically for sensors and actuator placement. Findings Unlike its other counterparts, the proposed method not only supports unstable systems but also requires significantly lower computations. The numerical investigations have confirmed the advantages of the proposed methods which are clearly significant, in particular, in dealing with large-scale unstable systems. Originality/value The proposed method is novel, and compared to the methods which have already been presented in literature is more general and numerically more efficient.
5

Johnson, Marty E., Luiz P. Nascimento, Mary Kasarda, and Chris R. Fuller. "The Effect of Actuator and Sensor Placement on the Active Control of Rotor Unbalance." Journal of Vibration and Acoustics 125, no. 3 (June 18, 2003): 365–73. http://dx.doi.org/10.1115/1.1569946.

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This paper investigates both theoretically and experimentally the effect of the location and number of sensors and magnetic bearing actuators on both global and local vibration reduction along a rotor using a feedforward control scheme. Theoretical approaches developed for the active control of beams have been shown to be useful as simplified models for the rotor scenario. This paper also introduces the time-domain LMS feedforward control strategy, used widely in the active control of sound and vibration, as an alternative control methodology to the frequency-domain feedforward approaches commonly presented in the literature. Results are presented showing that for any case where the same number of actuators and error sensors are used there can be frequencies at which large increases in vibration away from the error sensors can occur. It is also shown that using a larger number of error sensors than actuators results in better global reduction of vibration but decreased local reduction. Overall, the study demonstrated that an analysis of actuator and sensor locations when feedforward control schemes are used is necessary to ensure that harmful increased vibrations do not occur at frequencies away from rotor-bearing natural frequencies or at points along the rotor not monitored by error sensors.
6

Soman, Rohan, Kaleeswaran Balasubramaniam, Ali Golestani, Michał Karpiński, Pawel Malinowski, and Wieslaw Ostachowicz. "Actuator placement optimization for guided waves based structural health monitoring using fibre Bragg grating sensors." Smart Materials and Structures 30, no. 12 (November 1, 2021): 125011. http://dx.doi.org/10.1088/1361-665x/ac31c4.

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Abstract Structural health monitoring (SHM) systems have a potential to reduce lifecycle costs of structures. They may be used for maintenance planning which reduces the maintenance cost as well as for lifetime extension. As a result, there is a lot of active research in the area for SHM of civil and mechanical structures. The SHM system should be low cost, suitable for continuous monitoring, able to detect small levels of damage. Guided waves (GW) based SHM techniques allow monitoring of large plate-like structures with few sensors and have been identified as the most promising of techniques for SHM. Several different actuators and sensors have been developed and used for the GW based SHM. FBG sensors due to their low weight, and ability to be multiplexed have been long thought to be an ideal sensors for SHM. The recent development of the edge filtering approach has increased their sensitivity to GW sensing and made them ideal sensors. Unfortunately the FBG sensors are passive sensors and show directional sensitivity. These operational constraints make extension of the earlier developed GW based SHM techniques for FBG sensors difficult. Recently the authors developed a technique for damage detection specifically designed for a network with FBG sensors. This paper develops a methodology for a design of an actuator-sensor (AS) network for improving the damage assessment capability using the developed method. The paper develops a two-step methodology for the optimization of actuator placement for an AS network with FBG sensors. In the first step the number of actuators needed for the optimization are determined based on actuator densities. Once the number of actuators is known, a genetic algorithm (GA) is developed for the optimization of the their positions. The cost function is developed based on two new metrics (namely coverage2—coverage with at least 2 AS pairs and coverageR—radial coverage based on edge reflections) which are defined by the application demand. The optimized placement is then used to successfully detect and localize the damage. The study also shows the merit in the use of the specific metrics and the sufficiency of the metrics developed for improving the damage detection capability of the specific method.
7

Heck, L. P., J. A. Olkin, and K. Naghshineh. "Transducer Placement for Broadband Active Vibration Control Using a Novel Multidimensional QR Factorization." Journal of Vibration and Acoustics 120, no. 3 (July 1, 1998): 663–70. http://dx.doi.org/10.1115/1.2893881.

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This paper advances the state of the art in the selection of minimal configurations of sensors and actuators for active vibration control with smart structures. The method extends previous transducer selection work by (1) presenting a unified treatment of the selection and placement of large numbers of both sensors and actuators in a smart structure, (2) developing computationally efficient techniques to select the best sensor-actuator pairs for multiple unknown force disturbances exciting the structure, (3) selecting the best sensors and actuators over multiple frequencies, and (4) providing bounds on the performance of the transducer selection algorithms. The approach is based on a novel, multidimensional extension of the Householder QR factorization algorithm applied to the frequency response matrices that define the vibration control problem. The key features of the algorithm are its very low computational complexity, and a computable bound that can be used to predict whether the transducer selection algorithm will yield an optimal configuration before completing the search. Optimal configurations will result from the selection method when the bound is tight, which is the case for many practical vibration control problems. This paper presents the development of the method, as well as its application in active vibration control of a plate.
8

GAWRONSKI, W. "SIMULTANEOUS PLACEMENT OF ACTUATORS AND SENSORS." Journal of Sound and Vibration 228, no. 4 (December 1999): 915–22. http://dx.doi.org/10.1006/jsvi.1999.2466.

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9

Nandy, Animesh, Debabrata Chakraborty, and Mahesh S. Shah. "Optimal Sensors/Actuators Placement in Smart Structure Using Island Model Parallel Genetic Algorithm." International Journal of Computational Methods 16, no. 06 (May 27, 2019): 1840018. http://dx.doi.org/10.1142/s0219876218400182.

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Determination of optimal placements of sensors/actuators in large structures is a difficult job as large number of possible combinations leads to a very high computational time and storage. Therefore, this kind of optimization problem demands a parallel implementation of the optimization schemes. Island model genetic algorithm (GA) being inherently parallel has been used for searching optimal placements of collocated sensors/actuators. Numerical simulations have been done for determination of optimal placements of collocated PZT sensors and actuators in smart fiber reinforced shell structures using island model parallel GA (IMPGA) in conjunction with electro-mechanical finite element analysis with an objective of maximizing the controllability index. It has been observed that the present IMPGA-based formulation (due to its migration scheme) not only makes it possible to determine optimal sensors/actuators locations for large structures but also leads to a better solution at a much reduced and achievable computational time. Results from scalability analysis also show that the efficacy of the present method of using IMPGA for determination of optimal sensors/actuators location based on FEA will be more pronounced when actually used for real life problems requiring large number of sensors and actuators.
10

Huang, Xiu Feng, Ming Hong, and Hong Yu Cui. "The Optimal Location of Piezoelectric Sensor/Actuator Based on Adaptive Genetic Algorithm." Applied Mechanics and Materials 635-637 (September 2014): 799–804. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.799.

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This paper considered the optimal placement of collocated piezoelectric actuator-sensor pairs on a thin cantilever plate using a modal-based linear quadratic independent modal space controller. LQR performance was taken as objective for finding the optimal location of sensor–actuator pairs.The discrete optimal sensor and actuator location problem was formulated in the framework of a zero–one optimization problem,which was solved by real-coded adaptive genetic algorithm (AGA). The vibration response of the piezoelectric plate was calculated using the finite element method (FEM).The optimization and vibration control programs were written by FORTRAN language. The results of numrical examples show that the adaptive genetic algorithm based on the minimum of LQR performance for the optimal location of sensors and actuators is feasible and effective.
11

Sunar, M., K. Al-Athel, Bekir Sami Yilbas, H. Al-Qahtani, and T. Ayar. "Modeling and Placement of Thermopiezoelectro-Magnetic Materials." Advanced Materials Research 445 (January 2012): 520–25. http://dx.doi.org/10.4028/www.scientific.net/amr.445.520.

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There has been a vast interest in the general coupled field analysis of thermopiezoelectro-magnetic materials under which smart piezoelectric, thermopiezoelectric and magnetostrictive materials can be studied. The smart materials are often bonded as thin films on host structures for the purpose of sensing and/or actuation. It is well-known that the placement of sensors and actuators is important in order to obtain the appropriate sensor input and to provide the adequate actuation power. This study aims at modeling the important phenomenon of thermopiezoelectro-magnetism suitable for beam and/or plate type-host structures. The thermopiezoelectro-magnetic materials are modeled using the finite element method and the resulting equations are used for decision making on the best placement of the smart actuators on various host structures.
12

de Oliveira, Aguinaldo Soares, Douglas da Costa Ferreira, Fábio Roberto Chavarette, Nelson José Peruzzi, and Viviane Cassol Marques. "Piezoelectric Optimum Placement via LQR Controller." Advanced Materials Research 1077 (December 2014): 166–71. http://dx.doi.org/10.4028/www.scientific.net/amr.1077.166.

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The piezoelectric elements have received important attention from researchers because the piezoelectric materials are small, lightweight and resilient against adverse working environments and also piezoelectric materials can be used as both actuators and sensors. Actuators and sensors placement identification is a center study to avoid undesirable effects in flexible structure under control such as lack of observability and controllability system. In this research it was used a singular analysis of input control matrix as a piezoelectric placement tool and after piezoelectric placement study it was checked these positions through the piezoelectric elements placement in an optimum and no optimum positions and simulating the control through linear quadratic regulator technique in both positions. The flexible structure used as a model is a simply supported beam. As a main result the simulation demonstrate to be robust to piezoelectric placement identification.
13

Yao, Jun, Yan Fei Wu, and Huan Wang. "Optimal Design Method for Piezoelectric Sensors/Actuators Configuration." Advanced Materials Research 239-242 (May 2011): 815–20. http://dx.doi.org/10.4028/www.scientific.net/amr.239-242.815.

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In the active vibration control field, the piezoelectric element was extensively researched with the advantages of wide response frequency band, light weight, big driving force and good linearity, but they were mainly focused on the vibration suppression for smart structure and the study on the piezoelectric element used as excitation source in the vibration test was still limited. First, according to the electromechanical coupling equation of the piezoelectric material, the piezoelectric equation when the piezoelectric ceramic applied on the one-dimensional structure like beam was derived. Then the transfer functions from piezoelectric actuator to the piezoelectric sensor were established in cases of micro-element and limited size. The quasi-independent modal control method for piezoelectric beam was studied, which made several step modals being controlled by one group of piezoelectric film simultaneously is possible. And based on this, an optimal design method for placement of sensors/actuators in the vibration test in which the piezoelectric element was used as excitation source is found.
14

Morris, K. A. "Noise Reduction in Ducts Achievable by Point Control." Journal of Dynamic Systems, Measurement, and Control 120, no. 2 (June 1, 1998): 216–23. http://dx.doi.org/10.1115/1.2802412.

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Noise control in a one-dimensional duct is analyzed. This problem is of practical interest and is also simple enough that a complete theoretical analysis is possible. It is shown that the optimal controller leads to an unstable closed loop. The noise reduction level achievable with a stable closed loop is calculated for arbitrary choices of sensor and actuator locations. This enables the best placement of sensors and actuators to be determined. Also, the analysis indicates that a “spatial waterbed” effect exists in some configurations of active noise control: i.e., that noise levels are increased for points outside of the region over which the design is done.
15

Natarajan, Mahesh, Jonathan B. Freund, and Daniel J. Bodony. "Actuator selection and placement for localized feedback flow control." Journal of Fluid Mechanics 809 (November 18, 2016): 775–92. http://dx.doi.org/10.1017/jfm.2016.700.

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The selection and placement of actuators and sensors to control compressible viscous flows is addressed by developing a novel methodology based upon the eigensystem structural sensitivity of the linearized evolution operator appropriate for linear feedback control. Forward and adjoint global modes are used to construct a space of possible perturbations to the linearized operator, which yields a small optimization problem for selecting the parameters that best achieve the control objective, including where they should be placed. The method is demonstrated by informing actuation to suppress amplification of the instabilities in boundary layer separation in a high-subsonic diffuser. Complete stabilization is observed in the separated shear layer for short downstream distances at modest Reynolds number. Higher Reynolds numbers and longer distances are expected to be more challenging to stabilize; here the control informed by the procedure still substantively suppresses amplification of instabilities. It is also demonstrated that more complex actuator–sensor selections may not yield superior controllers.
16

Hu, Quan, and Jingrui Zhang. "Placement optimization of actuators and sensors for gyroelastic body." Advances in Mechanical Engineering 7, no. 3 (March 18, 2015): 168781401557376. http://dx.doi.org/10.1177/1687814015573765.

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17

Soubhia, Ana Luisa, and Alberto Luiz Serpa. "Discrete optimization for actuator and sensor positioning for vibration control using genetic algorithms." Journal of Vibration and Control 24, no. 17 (July 7, 2017): 4050–64. http://dx.doi.org/10.1177/1077546317718968.

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Research about actuator and sensor positioning is important to obtain smart structures that can achieve better performance, and studies concerning controller design techniques are also important. In some studies on smart structures, the positioning of sensors and actuators are defined by some physical criteria and, thereafter, the controller is designed to satisfy some requirements of the controlled system. However, the optimal number and placement of sensors and actuators can also be obtained through the solution of an optimization problem, taking into account, for example, the possible positions to allocate the active elements and the available number of these. This paper presents a discrete heuristic optimization technique in order to determine the discrete positions of the active elements in active control systems. Furthermore, a technique that involves the determination of the number of active elements and the positioning is shown. These techniques have been implemented based on the genetic algorithms. Depending on the desired number of the sensors and actuators, and the number of candidate positions, it is impractical to use a combinatorial algorithm, as this is very expensive in terms of computational time due to the number of possible combinations. Thus, the techniques developed here have the aim to obtain good solutions analyzing fewer combinations than the combinatorial method and in reduced computational time. In this paper, the controllers are designed based on the [Formula: see text] control theory. The objective function used to solve the positioning problem of active elements is the [Formula: see text] norm of the closed-loop system.
18

Thiene, Marco, Z. Sharif-Khodaei, and M. H. Aliabadi. "Optimal Sensor Placement for Damage Detection Based on Ultrasonic Guided Wave." Key Engineering Materials 665 (September 2015): 269–72. http://dx.doi.org/10.4028/www.scientific.net/kem.665.269.

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In this work a methodology for effective positioning of sensors and actuators for damage detection and characterisation is described. The novelty of the proposed methodology is that the fitness function to be optimised does not contain probability of detection (POD) which needs to be obtained for every possible sensor combination. The proposed fitness function is to provide the maximum coverage of the structure via Lamb waves and reduce the negative effects of boundary reflections. Once the fitness function is defines, genetic algorithm (GA) is used as an optimisation strategy to result in optimal sensor positioning.
19

Moutsopoulou, Amalia, Georgios E. Stavroulakis, Markos Petousis, Anastasios Pouliezos, and Nectarios Vidakis. "Optimal Placement and Active Control Methods for Integrating Smart Material in Dynamic Suppression Structures." Vibration 6, no. 4 (November 8, 2023): 975–1003. http://dx.doi.org/10.3390/vibration6040058.

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To simulate a lightweight structure with integrated actuators and sensors, two-dimensional finite elements are utilized. The study looks at the optimal location and active vibration control for a piezoelectric smart flexible structure. Intelligent applications are commonly used in engineering applications. In computational mechanics, selecting the ideal position for actuators to suppress oscillations is crucial. The structure oscillates due to dynamic disturbance, and active control is used to try to reduce the oscillation. Utilizing an LQR and Hinfinity controller, optimization is carried out to determine the best controller weights, which will dampen the oscillation. Challenging issues arise in the design of control techniques for piezoelectric smart structures. Piezoelectric materials have been investigated for use in distributed parameter systems (for example airplane wings, intelligent bridges, etc.) to provide active control efficiently and affordably. Still, no full suppression of the oscillation with this approach has been achieved so far. The controller’s order is then decreased using optimization techniques. Piezoelectric actuators are positioned optimally according to an enhanced optimization method. The outcomes demonstrate that the actuator optimization strategies used in the piezoelectric smart single flexible manipulator system have increased observability in addition to good vibration suppression results.
20

Shelley, F. J., and W. W. Clark. "Active Mode Localization in Distributed Parameter Systems with Consideration of Limited Actuator Placement, Part 2: Simulations and Experiments." Journal of Vibration and Acoustics 122, no. 2 (July 1, 1995): 165–68. http://dx.doi.org/10.1115/1.568454.

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The purpose of this two-part work is to apply active mode localization techniques to distributed parameter systems where control actuator and sensor placement is a limiting factor. In this paper, Part 2 of the study, the SVD eigenvector shaping technique examined in Part 1 is utilized to numerically and experimentally localize the response of a simply supported beam. This is done for two reasons. First, it demonstrates the application of this modified mode localization technique to a distributed parameter system. Second, it shows that it is possible to use this method to produce vibration isolation, reducing the absolute displacements in designated portions of the system while simultaneously curtailing the number of necessary control sensors and actuators. [S0739-3717(00)70302-3]
21

Abreu, Gustavo L. C. M., José F. Ribeiro, and Valder Steffen Jr. "Experiments on Optimal Vibration Control of a Flexible Beam Containing Piezoelectric Sensors and Actuators." Shock and Vibration 10, no. 5-6 (2003): 283–300. http://dx.doi.org/10.1155/2003/594083.

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In this paper, a digital regulator is designed and experimentally implemented for a flexible beam type structure containing piezoelectric sensors and actuators by using optimal control design techniques. The controller consists of a linear quadratic regulator with a state estimator, namely a Kalman observer. The structure is a cantilever beam containing a set of sensor/actuator PVDF/PZT ceramic piezoelectric patches bonded to the beam surface at the optimal location obtained for the first three vibration modes. The equations of motion of the beam are developed by using the assumed modes technique for flexible structures in infinite-dimensional models. This paper uses a method of minimizing the effect of the removed higher order modes on the low frequency dynamics of the truncated model by adding a zero frequency term to the low order model of the system. A measure of the controllability and observability of the system based on the modal cost function for flexible structures containing piezoelectric elements (intelligent structures) is used. The observability and controllability measures are determined especially to guide the placement of sensors and actuators, respectively. The experimental and numerical transfer functions are adjusted by using an optimization procedure. Experimental results illustrate the optimal control design of a cantilever beam structure.
22

Zhang, Xianmin, and Arthur G. Erdman. "Optimal Placement of Piezoelectric Sensors and Actuators for Controlled Flexible Linkage Mechanisms." Journal of Vibration and Acoustics 128, no. 2 (November 3, 2005): 256–60. http://dx.doi.org/10.1115/1.2159043.

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The optimal placement of sensors and actuators in active vibration control of flexible linkage mechanisms is studied. First, the vibration control model of the flexible mechanism is introduced. Second, based on the concept of the controllability and the observability of the controlled subsystem and the residual subsystem, the optimal model is developed aiming at the maximization of the controllability and the observability of the controlled modes and minimization of those of the residual modes. Finally, a numerical example is presented, which shows that the proposed method is feasible. Simulation analysis shows that to achieve the same control effect, the control system is easier to realize if the sensors and actuators are located in the optimal positions.
23

Burke, S. E., and J. E. Hubbard. "Spatial Filtering Concepts in Distributed Parameter Control." Journal of Dynamic Systems, Measurement, and Control 112, no. 4 (December 1, 1990): 565–73. http://dx.doi.org/10.1115/1.2896181.

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A new method of analyzing distributed parameter control systems is presented, based upon their input/output representation in a spatially and temporally transformed frequency space. The classes of distributed systems amenable to the analysis are described in terms of their Green’s functions. The plants’ input/output relations are studied in the transformed space using the singular value decomposition to determine the system’s spatial performance. Performance is quantified in terms of generalized command following, disturbance rejection, noise rejection, controllability, and observability over spatial and temporal bandwidths, with suitable design measures presented. The analysis provides insight into the performance of sensor and actuator distributions in achieving spatial frequency performance specifications, determines spatial regimes where the response is directional, and quantifies sensor and actuator placement with respect to limitations of system and transducer spatial modelling. The analysis is shown to be applicable to discrete as well as distributed sensors and actuators, and utilizes commonly available numerical analysis techniques. An example problem is considered.
24

Chhabra, Deepak, Gian Bhushan, and Pankaj Chandna. "Optimization of Collocated/Noncollocated Sensors and Actuators along with Feedback Gain Using Hybrid Multiobjective Genetic Algorithm-Artificial Neural Network." Chinese Journal of Engineering 2014 (February 20, 2014): 1–12. http://dx.doi.org/10.1155/2014/692140.

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A multiobjective optimization procedure is proposed to deal with the optimal number and locations of collocated/noncollocated sensors and actuators and determination of LQR controller gain simultaneously using hybrid multiobjective genetic algorithm-artificial neural network (GA-ANN). Multiobjective optimization problem has been formulated using trade-off objective functions ensuring good observability/controllability of the structure while minimizing the spillover effect and maximizing closed loop average damping ratio. Artificial neural networks (ANNs) are used to train the input as varying numbers and placements of sensors and actuators and the outputs are taken as the three objective functions (i.e., controllability, observability, and closed loop average damping ratio), thus forming three ANN models. The trained mathematical models of ANN are fed into the multiobjective GA. The hybrid multiobjective GA-ANN maintains the trade-off among the three objective functions. The ANN3 model is used experimentally to provide the control inputs to the piezoactuators. It is shown that the proposed method is effective in ascertaining the optimal number and placement of actuators and sensors with consideration of controllability, observability, and spillover prevention such that the performance on dynamic responses is also satisfied. It is also observed that damping ratio obtained with hybrid multiobjective GA-ANN and found with ANN experimentally/online holds well in agreement.
25

Hanagan, Linda M., Ernest C. Kulasekere, Kirthi S. Walgama, and Kamal Premaratne. "Optimal Placement of Actuators and Sensors for Floor Vibration Control." Journal of Structural Engineering 126, no. 12 (December 2000): 1380–87. http://dx.doi.org/10.1061/(asce)0733-9445(2000)126:12(1380).

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26

Seyed Sakha, Masoud, Hamid Reza Shaker, and Maryamsadat Tahavori. "Optimal sensors and actuators placement for large-scale switched systems." International Journal of Dynamics and Control 7, no. 1 (June 19, 2018): 147–56. http://dx.doi.org/10.1007/s40435-018-0446-7.

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27

Abdullah, Makola M., Andy Richardson, and Jameel Hanif. "Placement of sensors/actuators on civil structures using genetic algorithms." Earthquake Engineering & Structural Dynamics 30, no. 8 (2001): 1167–84. http://dx.doi.org/10.1002/eqe.57.

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28

Przystałka, Piotr, and Wojciech Moczulski. "Optimal Placement of Sensors and Actuators for Leakage Detection and Localization*." IFAC Proceedings Volumes 45, no. 20 (January 2012): 666–71. http://dx.doi.org/10.3182/20120829-3-mx-2028.00172.

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29

Letellier, Christophe, and Jean-Pierre Barbot. "Optimal flatness placement of sensors and actuators for controlling chaotic systems." Chaos: An Interdisciplinary Journal of Nonlinear Science 31, no. 10 (October 2021): 103114. http://dx.doi.org/10.1063/5.0055895.

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30

Xu, K., P. Warnitchai, and T. Igusa. "Optimal placement and gains of sensors and actuators for feedback control." Journal of Guidance, Control, and Dynamics 17, no. 5 (September 1994): 929–34. http://dx.doi.org/10.2514/3.21292.

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31

Sepulveda, Abdon E., and Lucien A. Schmit. "Optimal placement of actuators and sensors in control-augmented structural optimization." International Journal for Numerical Methods in Engineering 32, no. 6 (October 25, 1991): 1165–87. http://dx.doi.org/10.1002/nme.1620320602.

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32

Kędziora, Piotr. "Optimal Design of PZT Actuators and Sensors in Composite Structural Elements." Key Engineering Materials 542 (February 2013): 59–73. http://dx.doi.org/10.4028/www.scientific.net/kem.542.59.

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In this paper, the various optimization criteria used for optimal placement of piezoelectric actuators on laminated structures are discussed. Piezoelectric materials are used as layers or fibers that are embedded within or bonded to the surfaces of a structure. The present formulation of optimal design introduces also boundaries of piezoelectric patches as new class of design variables.
33

Shelley, Franz J., and William W. Clark. "Active Mode Localization in Distributed Parameter Systems with Consideration of Limited Actuator Placement, Part 1: Theory." Journal of Vibration and Acoustics 122, no. 2 (July 1, 1995): 160–64. http://dx.doi.org/10.1115/1.568453.

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The purpose of this two-part work is to apply active mode localization to distributed parameter systems where the number of control sensors and actuators is a limiting factor. In this part, the theoretical development portion of the study, two approaches are presented that shape system eigenvectors using feedback control, generating localization to produce areas of isolation with relatively low vibration amplitudes compared to other parts of the structure. The first approach uniformly shapes all eigenvectors of a vibrating system, but can require many actuators to do so. The second more general approach uses singular value decomposition (SVD) to shape selected eigenvectors of a system, localizing the response of these modes to any disturbance, and requiring few actuators. [S0739-3717(00)70202-9]
34

Lee, An-Chen, and Song-Tsuen Chen. "Collocated Sensor/Actuator Positioning and Feedback Design in the Control of Flexible Structure System." Journal of Vibration and Acoustics 116, no. 2 (April 1, 1994): 146–54. http://dx.doi.org/10.1115/1.2930405.

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This paper presents a new control design method for the control of flexible systems that not only guarantees closed-loop asymptotic stability but also effectively suppresses vibration. This method allows integrated determination of actuator/sensor locations and feedback gain via minimization of an energy criterion, which is chosen as the integrated total energy stored in the system. The energy criterion is determined via an efficient solution of the Lyapunov equation and minimized with a quasi-Newton or recursive quadratic programming algorithm. The prerequisite for this optimal design method is that the controlled system be asymptotically stable. This study shows that when the controller structure is a collocated direct velocity feedback design with positive definite feedback gain, the number and placement of actuators/sensors are the only factors needed to determine necessary and sufficient conditions for ensuring closed-loop asymptotic stability. The application of this method to a simple flexible structure confirms the direct relationship between our optimization criterion and effectiveness in vibration suppression.
35

SHIMOMURA, Takashi, Masahiro TAKAHASHI, and Takao FUJII. "Strictly Positive Real H2 Controller Synthesis with Placement of Sensors and Actuators." Transactions of the Society of Instrument and Control Engineers 44, no. 4 (2008): 309–16. http://dx.doi.org/10.9746/ve.sicetr1965.44.309.

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36

Jia, Shiyuan, Yinghong Jia, Shijie Xu, and Quan Hu. "Optimal Placement of Sensors and Actuators for Gyroelastic Body Using Genetic Algorithms." AIAA Journal 54, no. 8 (August 2016): 2472–88. http://dx.doi.org/10.2514/1.j054696.

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37

KAWABATA, Nariyuki, and Hisao FUKUNAGA. "Placement of Actuators and Sensors for Static Shape Control of Truss Structures." Transactions of the Japan Society of Mechanical Engineers Series C 68, no. 667 (2002): 855–61. http://dx.doi.org/10.1299/kikaic.68.855.

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38

VIRGALA, IVAN, MICHAL KELEMEN, TATIANA KELEMENOVA, LUBICA MIKOVA, ERIK PRADA, STEFAN GRUSHKO, MARTIN VARGA, PETER JAN SINCAK, TOMAS MERVA, and ZDENKO BOBOVSKY. "BIPED ROBOT WITH UNCONVENTIONAL KINEMATICS." MM Science Journal 2022, no. 3 (September 27, 2022): 5819–24. http://dx.doi.org/10.17973/mmsj.2022_10_2022092.

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The article deals with the design of a robot with an unconventional kinematic structure, which is able to vertically stabilize the position of the robot base for the placement of sensors and handling superstructures. The robot concept was designed to have as few actuators as possible. The robot's kinematics was solved for the purpose of simulating the robot's movement and implementation into the robot's control system.
39

Flynn, Eric B., and Michael D. Todd. "Optimal Placement of Piezoelectric Actuators and Sensors for Detecting Damage in Plate Structures." Journal of Intelligent Material Systems and Structures 21, no. 3 (June 4, 2009): 265–74. http://dx.doi.org/10.1177/1045389x09338080.

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40

KAMEYAMA, Masaki, and Masato HARADA. "B210 Consideration on the Optimal Placement Method of Sensors/Actuators for Vibration Control." Proceedings of the Symposium on the Motion and Vibration Control 2011.12 (2011): 343–46. http://dx.doi.org/10.1299/jsmemovic.2011.12.343.

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41

Greene, Brian R., Antonio R. Segales, Sean Waugh, Simon Duthoit, and Phillip B. Chilson. "Considerations for temperature sensor placement on rotary-wing unmanned aircraft systems." Atmospheric Measurement Techniques 11, no. 10 (October 10, 2018): 5519–30. http://dx.doi.org/10.5194/amt-11-5519-2018.

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Abstract. Integrating sensors with a rotary-wing unmanned aircraft system (rwUAS) can introduce several sources of biases and uncertainties if not properly accounted for. To maximize the potential for rwUAS to provide reliable observations, it is imperative to have an understanding of their strengths and limitations under varying environmental conditions. This study focuses on the quality of measurements relative to sensor locations on board rwUAS. Typically, thermistors require aspiration and proper siting free of heat sources to make representative measurements of the atmosphere. In an effort to characterize ideal locations for sensor placement, a series of experiments were conducted in the homogeneous environment of an indoor chamber with a pedestal-mounted rwUAS. A suite of thermistors along with a wind probe were mounted inside of a solar shield, which was affixed to a linear actuator arm. The actuator arm was configured such that the sensors within the solar shield would travel underneath the platform into and out of the propeller wash. The actuator arm was displaced horizontally underneath the platform while the motors were throttled to 50 %, yielding a time series of temperature and wind speed that could be compared to temperatures being collected in the ambient environment. Results indicate that temperatures may be biased in the order of 0.5–1.0 ∘C and vary appreciably without aspiration, sensors placed close to the tips of the rotors may experience biases due to frictional and compressional heating as a result of turbulent fluctuations, and sensors in proximity to motors may experience biases approaching 1 ∘C. From these trials, it has been determined that sensor placement underneath a propeller on an rwUAS a distance of one quarter the length of the propeller from the tip is most likely to be minimally impacted from influences of turbulence and motor, compressional, and frictional heating while still maintaining adequate airflow. When opting to use rotor wash as a means for sensor aspiration, the user must be cognizant of these potential sources of platform-induced heating when determining sensor location.
42

Gupta, Nakul, Gopu Srilekha, Karabi Kalita Das, Radha Goel, Muthana Saleh Mashkour, and Manish Kumar. "Novel Manufacturing Techniques for Multifunctional Composites: Integration of Sensors and Actuators." E3S Web of Conferences 430 (2023): 01117. http://dx.doi.org/10.1051/e3sconf/202343001117.

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In the rapidly evolving realm of advanced materials, multifunctional composites have emerged as a pivotal frontier, offering unprecedented capabilities in structural and functional integration. This research delves into innovative manufacturing techniques tailored for the seamless integration of sensors and actuators within these composites. Traditional manufacturing methods often compromise the intrinsic properties of composites when embedding functional elements. To address this, our study introduces a novel approach that synergistically combines additive manufacturing and nanotechnology, ensuring the preservation of structural integrity while enhancing functionality. We demonstrate that through strategic placement and orientation of sensors and actuators, it is possible to achieve real-time monitoring, adaptive response, and self-healing capabilities in the composite structures. The developed techniques not only bolster the mechanical performance but also endow the composites with smart functionalities, paving the way for their application in next-generation aerospace, automotive, and biomedical sectors. This paper elucidates the underlying principles, the meticulous process optimizations, and potential applications, marking a significant stride in the convergence of materials science and intelligent systems.
43

Zhang, Peijin, Chengyang Ding, Yunlang Xu, Runze Ding, Xiaofeng Yang, and Hongliang Lu. "A Co-Optimization Method of Actuators/Sensors Placement and LQG Controller for Vibration Suppression." IEEE Access 9 (2021): 29482–89. http://dx.doi.org/10.1109/access.2021.3058692.

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44

Liu, XiaoXiang, and Jun Hu. "On the placement of actuators and sensors for flexible structures with closely spaced modes." Science China Technological Sciences 53, no. 7 (July 2010): 1973–82. http://dx.doi.org/10.1007/s11431-010-4028-y.

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45

TANIGUCHI, Hironobu, Masaki KAMEYAMA, Van Nha NGUYEN, and Yusuke NAMITA. "Trade-off Analysis on Optimal Placement of Sensors/Actuators for Multi-modal Vibration Control." Proceedings of Conference of Hokuriku-Shinetsu Branch 2017.54 (2017): K044. http://dx.doi.org/10.1299/jsmehs.2017.54.k044.

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46

Molter, Alexandre, Otávio A. Alves da Silveira, Jun S. Ono Fonseca, and Valdecir Bottega. "Simultaneous Piezoelectric Actuator and Sensor Placement Optimization and Control Design of Manipulators with Flexible Links Using SDRE Method." Mathematical Problems in Engineering 2010 (2010): 1–23. http://dx.doi.org/10.1155/2010/362437.

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This paper presents a control design for flexible manipulators using piezoelectric actuators bonded on nonprismatic links. The dynamic model of the manipulator is obtained in a closed form through the Lagrange equations. Each link is discretized using finite element modal formulation based on Euler-Bernoulli beam theory. The control uses the motor torques and piezoelectric actuators for controlling vibrations. An optimization problem with genetic algorithm (GA) is formulated for the location and size of the piezoelectric actuator and sensor on the links. The natural frequencies and mode shapes are computed by the finite element method, and the irregular beam geometry is approximated by piecewise prismatic elements. The State-Dependent Riccati Equation (SDRE) technique is used to derive a suboptimal controller for a robot control problem. A state-dependent equation is solved at each new point obtained for the variables from the problem, along the trajectory to obtain a nonlinear feedback controller. Numerical tests verify the efficiency of the proposed optimization and control design.
47

Yan, Tian-Hong, and Rong-Ming Lin. "General optimization of sizes or placement for various sensors/actuators in structure testing and control." Smart Materials and Structures 15, no. 3 (April 25, 2006): 724–36. http://dx.doi.org/10.1088/0964-1726/15/3/008.

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48

Sethi, V., and G. Song. "Pole-Placement Vibration Control of a Flexible Composite I-beam using Piezoceramic Sensors and Actuators." Journal of Thermoplastic Composite Materials 19, no. 3 (May 2006): 293–307. http://dx.doi.org/10.1177/0892705706062187.

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49

KAMEYAMA, Masaki, and Van Nha NGUYEN. "1512 Consideration on the Optimal Placement Method of Sensors/Actuators for Multi-modal Vibration Control." Proceedings of Design & Systems Conference 2015.25 (2015): _1512–1_—_1512–10_. http://dx.doi.org/10.1299/jsmedsd.2015.25._1512-1_.

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50

Sinha, S., U. Vaidya, and R. Rajaram. "Operator theoretic framework for optimal placement of sensors and actuators for control of nonequilibrium dynamics." Journal of Mathematical Analysis and Applications 440, no. 2 (August 2016): 750–72. http://dx.doi.org/10.1016/j.jmaa.2016.03.058.

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