Log in

Relevant bibliographies by topics / Dynamic system identification / Journal articles

Journal articles on the topic 'Dynamic system identification'

To see the other types of publications on this topic, follow the link: Dynamic system identification.

Author: Grafiati

Published: 10 December 2022

Last updated: 19 February 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Dynamic system identification.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Hollowell, William T., Walter D. Pilkey, and Edwin M. Sieveka. "System identification of dynamic structures." Finite Elements in Analysis and Design 4, no. 1 (June 1988): 65–77. http://dx.doi.org/10.1016/0168-874x(88)90024-8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Denno, Peter, Charles Dickerson, and Jennifer Anne Harding. "Dynamic production system identification for smart manufacturing systems." Journal of Manufacturing Systems 48 (July 2018): 192–203. http://dx.doi.org/10.1016/j.jmsy.2018.04.006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Alci, Musa. "New dynamic fuzzy structure and dynamic system identification." Soft Computing 10, no. 2 (April 13, 2005): 87–93. http://dx.doi.org/10.1007/s00500-004-0428-x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Fang, Pan, Liming Dai, Yongjun Hou, Mingjun Du, and Wang Luyou. "The Study of Identification Method for Dynamic Behavior of High-Dimensional Nonlinear System." Shock and Vibration 2019 (March 7, 2019): 1–9. http://dx.doi.org/10.1155/2019/3497410.

Full text

Abstract:

The dynamic behavior of nonlinear systems can be concluded as chaos, periodicity, and the motion between chaos and periodicity; therefore, the key to study the nonlinear system is identifying dynamic behavior considering the different values of the system parameters. For the uncertainty of high-dimensional nonlinear dynamical systems, the methods for identifying the dynamics of nonlinear nonautonomous and autonomous systems are treated. In addition, the numerical methods are employed to determine the dynamic behavior and periodicity ratio of a typical hull system and Rössler dynamic system, respectively. The research findings will develop the evaluation method of dynamic characteristics for the high-dimensional nonlinear system.

APA, Harvard, Vancouver, ISO, and other styles

5

Greblicki, W., and M. Pawlak. "Dynamic system identification with order statistics." IEEE Transactions on Information Theory 40, no. 5 (1994): 1474–89. http://dx.doi.org/10.1109/18.333862.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Yamada, T., and T. Yabuta. "Dynamic system identification using neural networks." IEEE Transactions on Systems, Man, and Cybernetics 23, no. 1 (1993): 204–11. http://dx.doi.org/10.1109/21.214778.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Heij, C., and W. Scherrer. "System Identification by Dynamic Factor Models." SIAM Journal on Control and Optimization 35, no. 6 (November 1997): 1924–51. http://dx.doi.org/10.1137/s0363012995282127.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Roberts, D. E., and N. C. Hay. "Dynamic response simulation through system identification." Journal of Sound and Vibration 295, no. 3-5 (August 2006): 1017–27. http://dx.doi.org/10.1016/j.jsv.2006.02.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Kong, Mingfang, Bingzhen Chen, Xiaorong He, and Shanying Hu. "Gross error identification for dynamic system." Computers & Chemical Engineering 29, no. 1 (December 2004): 191–97. http://dx.doi.org/10.1016/j.compchemeng.2004.07.008.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Ergon, Rolf. "Dynamic system multivariate calibration by system identification methods." Modeling, Identification and Control: A Norwegian Research Bulletin 19, no. 2 (1998): 77–97. http://dx.doi.org/10.4173/mic.1998.2.2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Betta, A., and D. A. Linkens. "Intelligent knowledge-based system for dynamic system identification." IEE Proceedings D Control Theory and Applications 137, no. 1 (1990): 1. http://dx.doi.org/10.1049/ip-d.1990.0001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Ahn, T. Y., K. F. Eman, and S. M. Wu. "Cutting Dynamics Identification by Dynamic Data System (DDS) Modeling Approach." Journal of Engineering for Industry 107, no. 2 (May 1, 1985): 91–94. http://dx.doi.org/10.1115/1.3185988.

Full text

Abstract:

The dynamics of the cutting process have been conventionally characterized in terms of the Dynamic Cutting Force Coefficients (DCFC) which represent its transfer characteristics at discrete frequencies. However, this approach fails to obtain the transfer function of the process in closed analytical form. Anticipating the stochastic nature of the cutting process and the double modulation principle, a two-input one-output multivariate system was postulated for the dynamic cutting process identification model. The Dynamic Data System (DDS) methodology was used to formulate and characterize the dynamic cutting process using Modified Autoregressive Moving Average Vector (MARMAV) models. Subsequently, transfer functions of the inner and outer modulation dynamics of the cutting processes were obtained from the identified models.

APA, Harvard, Vancouver, ISO, and other styles

13

Cheng, Jun, Shusheng Bi, Chang Yuan, Lin Chen, Yueri Cai, and Yanbin Yao. "A Graph Theory-Based Method for Dynamic Modeling and Parameter Identification of 6-DOF Industrial Robots." Applied Sciences 11, no. 22 (November 19, 2021): 10988. http://dx.doi.org/10.3390/app112210988.

Full text

Abstract:

At present, the absolute positioning accuracy and control accuracy of industrial serial robots need to be improved to meet the accuracy requirements of precision manufacturing and precise control. An accurate dynamic model is an important theoretical basis for solving this problem, and precise dynamic parameters are the prerequisite for precise control. The research of dynamics and parameter identification can greatly promote the application of robots in the field of precision manufacturing and automation. In this paper, we study the dynamical modeling and dynamic parameter identification of an industrial robot system with six rotational DOF (6R robot system) and propose a new method for identifying dynamic parameters. Our aim is to provide an accurate mathematical description of the dynamics of the 6R robot and to accurately identify its dynamic parameters. First, we establish an unconstrained dynamic model for the 6R robot system and rewrite it to obtain the dynamic parameter identification model. Second, we establish the constraint equations of the 6R robot system. Finally, we establish the dynamic model of the constrained 6R robot system. Through the ADAMS simulation experiment, we verify the correctness and accuracy of the dynamic model. The experiments prove that the result of parameter identification has extremely high accuracy and the dynamic model can accurately describe the 6R robot system mathematically. The dynamic modeling method proposed in this paper can be used as the theoretical basis for the study of 6R robot system dynamics and the study of dynamics-based control theory.

APA, Harvard, Vancouver, ISO, and other styles

14

Chawla, Ishan, and Ashish Singla. "ANFIS based system identification of underactuated systems." International Journal of Nonlinear Sciences and Numerical Simulation 21, no. 7-8 (November 18, 2020): 649–60. http://dx.doi.org/10.1515/ijnsns-2018-0005.

Full text

Abstract:

AbstractIn this work, the effectiveness of the adaptive neural based fuzzy inference system (ANFIS) in identifying underactuated systems is illustrated. Two case studies of underactuated systems are used to validate the system identification i. e., linear inverted pendulum (LIP) and rotary inverted pendulum (RIP). Both the systems are treated as benchmark systems in modeling and control theory for their inherit nonlinear, unstable, and underactuated behavior. The systems are modeled with ANFIS using the input-output data acquired from the dynamic response of the nonlinear analytical model of the systems. The dynamic response of the ANFIS model is simulated and compared to the nonlinear mathematical model of the inverted pendulum systems. In order to check the effectiveness of the ANFIS model, mean square error is used as the performance index. From the obtained simulation results, it has been perceived that the ANFIS model performed satisfactorily within the trained operating range while a minor deviation is seen outside the trained operating region for both the case studies. Furthermore, the experimental validation of the of the proposed ANFIS model is done by comparing it with the experimental model of the rotary inverted pendulum. The obtained results show that the response of ANFIS model is in close agreement to the experimental model of the rotary inverted pendulum.

APA, Harvard, Vancouver, ISO, and other styles

15

Pawłowicz, Bartosz, Bartosz Trybus, Mateusz Salach, and Piotr Jankowski-Mihułowicz. "Dynamic RFID Identification in Urban Traffic Management Systems." Sensors 20, no. 15 (July 29, 2020): 4225. http://dx.doi.org/10.3390/s20154225.

Full text

Abstract:

The paper covers the application of Radio Frequency IDentification (RFID) technology in road traffic management with regard to vehicle identification. Various infrastructure configurations for Automated Vehicle Identification (AVI) have been presented, including configurations that can be used in urban traffic as part of the Smart City concept. In order to describe the behavior of multiple identifications of moving vehicles, an operation model of the dynamic identification using RFID is described. While it extends the definition of the correct work zone, this paper introduces the concept of dividing the zone into sections corresponding to so-called inventory rounds. The system state is described using a set of matrices in which unread, read, and lost transponders are recorded in subsequent rounds and sections. A simplified algorithm of the dynamic object identification system was also proposed. The results of the simulations and lab experiments show that the efficiency of mobile object identification is conditioned by the parameters of the communication protocol, the speed of movement, and the number of objects.

APA, Harvard, Vancouver, ISO, and other styles

16

Xia, Qing, Yun Lin, and Hui Luo. "Dynamic RLS-DCD for Sparse System Identification." Applied Mechanics and Materials 602-605 (August 2014): 2411–14. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.2411.

Full text

Abstract:

In this passage we propose a computationally efficient adaptive filtering algorithm for sparse system identification.The algorithm is based on dichotomous coordinate descent iterations, reweighting iterations,iterative support detection.In order to reduce the complexity we try to discuss in the support.we suppose the support is partial,and partly erroneous.Then we can use the iterative support detection to solve the problem.Numerical examples show that the proposed method achieves an identification performance better than that of advanced sparse adaptive filters (l1-RLS,l0-RLS) and its performance is close to the oracle performance.

APA, Harvard, Vancouver, ISO, and other styles

17

Bai, Zhe, Eurika Kaiser, Joshua L. Proctor, J. Nathan Kutz, and Steven L. Brunton. "Dynamic Mode Decomposition for Compressive System Identification." AIAA Journal 58, no. 2 (February 2020): 561–74. http://dx.doi.org/10.2514/1.j057870.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

ADWANKAR, SANDEEP, and RAVI N. BANAVAR. "A recurrent network for dynamic system identification." International Journal of Systems Science 28, no. 12 (July 1997): 1239–50. http://dx.doi.org/10.1080/00207729708929481.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Kim, Soo-Yong, Keunhwi Koo, Junho Huh, Hyeonwoo Cho, and Sang Woo Kim. "System Identification Using Embedded Dynamic Signal Analyzer." Japanese Journal of Applied Physics 51, no. 8S2 (August 1, 2012): 08JB02. http://dx.doi.org/10.7567/jjap.51.08jb02.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Kim, Soo-Yong, Keunhwi Koo, Junho Huh, Hyeonwoo Cho, and Sang Woo Kim. "System Identification Using Embedded Dynamic Signal Analyzer." Japanese Journal of Applied Physics 51 (August 20, 2012): 08JB02. http://dx.doi.org/10.1143/jjap.51.08jb02.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Kosaka, Manabu, Hiroshi Uda, Eiichi Bamba, and Hiroshi Shibata. "Dynamic System Identification Using a Step Input." Journal of Low Frequency Noise, Vibration and Active Control 24, no. 2 (June 2005): 125–34. http://dx.doi.org/10.1260/0263092054530948.

Full text

Abstract:

In this paper, we propose a deterministic off-line identification method performed by using input and output data with a constant steady state output response such as a step response that causes noise or vibration from a mechanical system at the moment when it is applied but they are attenuated asymptotically. The method can directly acquire any order of reduced model without knowing the real order of a plant, in such a way that the intermediate parameters are uniquely determined so as to be orthogonal with respect to 0 ∼ N-tuple integral values of output error and irrelevant to the unmodelled dynamics. From the intermediate parameters, the coefficients of a rational transfer function are calculated. In consequence, the method can be executed for any plant without knowing or estimating its order at the beginning. The effectiveness of the method is illustrated by numerical simulations and also by applying it to a 2-mass system.

APA, Harvard, Vancouver, ISO, and other styles

22

Bogdan Proca, Amuliu, and Ali Keyhani. "Identification of power steering system dynamic models." Mechatronics 8, no. 3 (April 1998): 255–70. http://dx.doi.org/10.1016/s0957-4158(98)00003-8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Beghelli, S., R. P. Guidorzi, and U. Soverini. "The frisch scheme in dynamic system identification." Automatica 26, no. 1 (January 1990): 171–76. http://dx.doi.org/10.1016/0005-1098(90)90168-h.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Li, Xiaoou, and Wen Yu. "Dynamic system identification via recurrent multilayer perceptrons." Information Sciences 147, no. 1-4 (November 2002): 45–63. http://dx.doi.org/10.1016/s0020-0255(02)00207-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Ge, Ma, and Eric M. Lui. "Structural damage identification using system dynamic properties." Computers & Structures 83, no. 27 (October 2005): 2185–96. http://dx.doi.org/10.1016/j.compstruc.2005.05.002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Netto, Winston, Eloy Pena Asensio, S. Meenatchi Sundaram, and C. R. Srinivasan. "LMS Filter Based Frequency Domain System Identification of Mass – Spring – Damper System with Varying Dynamics." International Journal of Mechanics 16 (January 17, 2022): 1–5. http://dx.doi.org/10.46300/9104.2022.16.1.

Full text

Abstract:

All practical and real-time systems are non-linear and dynamic in nature. In the majority of studies associated with systems, it is assumed that the system is linear and the dynamics of the system is remaining constant. Though these assumptions help in easier mathematical formulations of the systems, it also imposes a lot of restrictions on understanding the system completely and its behavior in depth. In this study, the focus is on capturing the varying dynamics of Mass - Spring – Damper system using a frequency domain-based system identification approach. The system identification technique is based on an adaptive filter which is implemented through MATLAB software.

APA, Harvard, Vancouver, ISO, and other styles

27

Pramod, B. R., and S. C. Bose. "System Identification Using ARMA Modeling and Neural Networks." Journal of Engineering for Industry 115, no. 4 (November 1, 1993): 487–91. http://dx.doi.org/10.1115/1.2901794.

Full text

Abstract:

Stochastic system identification is an important tool for control of discrete dynamic systems. Among the modeling strategies developed for this purpose, Auto Regressive Moving Average (ARMA for discrete systems) models offer an accurate identification technique. The disadvantage with these models are that they are extremely complicated to implement on-line, especially for nonlinear time-variant systems. This paper utilizes a Neural Network structure for identification of stochastic processes and tracks system dynamics by on-line adjustments of network parameters. Neural dynamics is based on impulse responses and an iterative learning algorithm is derived using conventional principles of gradient descent and backpropagation. The learning algorithm is analyzed and shown to be fast and accurate in the identification of parameters for stochastic processes in both time-invariant and time-variant cases.

APA, Harvard, Vancouver, ISO, and other styles

28

T.K., Khadeeja Nusrath, Lulu V.P., and Jatinder Singh. "System identification of flybar-less rotorcraft UAV." Aircraft Engineering and Aerospace Technology 92, no. 10 (August 10, 2020): 1483–93. http://dx.doi.org/10.1108/aeat-05-2019-0100.

Full text

Abstract:

Purpose This paper aims to build an accurate mathematical model which is necessary for control design and attitude estimation of a miniature unmanned rotorcraft and its subsequent conversion to an autonomous vehicle. Design/methodology/approach Frequency-domain system identification of a small-size flybar-less remote controlled helicopter is carried out based on the input–output data collected from flight tests of the instrumented vehicle. A complete six degrees of freedom quasi-steady dynamic model is derived for hover and cruise flight conditions. Findings The veracity of the developed model is ascertained by comparing the predicted model responses to the actual responses from flight experiments and from statistical measures. Dynamic stability analysis of the vehicle is carried out using eigenvalues and eigenvectors. The identified model represents the vehicle dynamics very well in the frequency range of interest. Research limitations/implications The model needs to be augmented with additional terms to represent the high-frequency dynamics of the vehicle. Practical implications Control algorithms developed using the first principles model can be easily reconfigured using the identified model, because the model structure is not altered during identification. Originality/value This paper gives a practical solution for model identification and stability analysis of a small-scale flybar-less helicopter. The estimated model can be easily used in developing control algorithms.

APA, Harvard, Vancouver, ISO, and other styles

29

ROY, SAJAL, and SUBRATA CHAKRABORTY. "IDENTIFICATION OF DAMPING PARAMETERS OF LINEAR DYNAMIC SYSTEM." International Journal of Structural Stability and Dynamics 09, no. 03 (September 2009): 473–87. http://dx.doi.org/10.1142/s0219455409003132.

Full text

Abstract:

The identification of damping in a structural system is extremely important for reliable prediction of response of dynamic systems. The present study focuses on the damping parameters identification of linear multi-degree of freedom (MDOF) systems defined by Rayleigh damping model. A system identification (SI) algorithm based on the free vibration response of structures is proposed to identify the damping parameters. In doing so, the equation error based SI approach is readily developed for identification of damping properties at element level. The formulation is elucidated using numerically simulated modal data obtained through finite element analysis. The robustness of the algorithm is also studied by Monte Carlo simulation based error sensitivity analysis. The identified damping values are found to be in consistent with those of pre-assigned values used to simulate modal data.

APA, Harvard, Vancouver, ISO, and other styles

30

Dantas, Tarcísio Soares Siqueira, Ivan Carlos Franco, Ana Maria Frattini Fileti, and Flávio Vasconcelos da Silva. "Nonlinear System Identification of a Refrigeration System." International Journal of Air-Conditioning and Refrigeration 24, no. 04 (December 2016): 1650024. http://dx.doi.org/10.1142/s2010132516500243.

Full text

Abstract:

Applications of advanced control algorithms are important in the refrigeration field to achieve low-energy costs and accurate set-point tracking. However, the designing and tuning of control systems depend on dynamic mathematical models. Approaches like analytical modeling can be time-consuming because they usually lead to a large number of differential equations with unknown parameters. In this work, the application of system identification with the fast recursive orthogonal least square (FROLS) algorithm is proposed as an alternative to analytical modeling to develop a process dynamic model. The evaporating temperature (EVT), condensing temperature (CDT) and useful superheat (USH) are the outputs of interest for this system; covariance analysis of the candidate inputs shows that the model should be single-input–single-output (SISO). Good simulation results are obtained with two different validation data, with average output errors of 0.0343 (EVT model), 0.0079 (CDT model) and 0.1578 (USH model) for one of the datasets, showing that this algorithm is a valid alternative for modeling refrigeration systems.

APA, Harvard, Vancouver, ISO, and other styles

31

Benz, J., J. Polster, R. Bär, and G. Gauglitz. "Program system SIDYS: Simulation and parameter identification of dynamic systems." Computers & Chemistry 11, no. 1 (January 1987): 41–48. http://dx.doi.org/10.1016/0097-8485(87)80007-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Rusanov, V. A., A. V. Daneev, Yu É. Linke, V. N. Sizykh, and V. A. Voronov. "SYSTEM-THEORETICAL FOUNDATIONS FOR IDENTIFICATION OF DYNAMIC SYSTEMS (PART I)." Far East Journal of Mathematical Sciences (FJMS) 106, no. 1 (September 1, 2018): 1–42. http://dx.doi.org/10.17654/ms106010001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Rusanov, V. A., A. V. Daneev, A. V. Lakeyev, Yu É. Linke, and A. A. Vetrov. "SYSTEM-THEORETICAL FOUNDATIONS FOR IDENTIFICATION OF DYNAMIC SYSTEMS (PART II)." Far East Journal of Mathematical Sciences (FJMS) 116, no. 1 (July 30, 2019): 25–68. http://dx.doi.org/10.17654/ms116010025.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Chiuso, Alessandro, Riccardo Muradore, and Enrico Marchetti. "Dynamic Calibration of Adaptive Optics Systems: A System Identification Approach." IEEE Transactions on Control Systems Technology 18, no. 3 (May 2010): 705–13. http://dx.doi.org/10.1109/tcst.2009.2023914.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Luo, Z. P., and S. M. Wu. "Boiling Water Reactor Dynamics Identification by the Dynamic Data System Methodology." Nuclear Science and Engineering 94, no. 1 (September 1986): 12–23. http://dx.doi.org/10.13182/nse86-a17112.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Jiang, Jinhui, Shuyi Luo, and Zhongzai Liang. "Online identification of impact loads of multi-degree-of-freedom system based on Kalman filter." International Journal of Applied Electromagnetics and Mechanics 64, no. 1-4 (December 10, 2020): 359–67. http://dx.doi.org/10.3233/jae-209341.

Full text

Abstract:

Dynamic load identification is the second kind of inverse problem in structural dynamics. It is a process of reconstructing load applied to structure in case of structural dynamic model and information of structural response. Online identification is one of the frontier problems in dynamic load identification, which has high difficulty and broad application prospects. In this paper, an online identification of dynamic load of the multi-degree-of-freedom system based on Kalman filter in modal space is proposed. Since the Kalman filter has excellent real-time performance and robustness, it is possible to be used in dynamic load online identification. We start from the theoretical derivation in detail for the multi-degree-of-freedom system, then the feasibility and effectiveness of the method is verified by numerical simulation of three-degree-of-freedom system with the single impact load and continuous multiple impact load.

APA, Harvard, Vancouver, ISO, and other styles

37

Heiland, Jan, and Benjamin Unger. "Identification of Linear Time-Invariant Systems with Dynamic Mode Decomposition." Mathematics 10, no. 3 (January 28, 2022): 418. http://dx.doi.org/10.3390/math10030418.

Full text

Abstract:

Dynamic mode decomposition (DMD) is a popular data-driven framework to extract linear dynamics from complex high-dimensional systems. In this work, we study the system identification properties of DMD. We first show that DMD is invariant under linear transformations in the image of the data matrix. If, in addition, the data are constructed from a linear time-invariant system, then we prove that DMD can recover the original dynamics under mild conditions. If the linear dynamics are discretized with the Runge–Kutta method, then we further classify the error of the DMD approximation and detail that for one-stage Runge–Kutta methods; even the continuous dynamics can be recovered with DMD. A numerical example illustrates the theoretical findings.

APA, Harvard, Vancouver, ISO, and other styles

38

Alqahtani, Ayedh, Mohammad Alsaffar, Mohamed El-Sayed, and Bader Alajmi. "Data-Driven Photovoltaic System Modeling Based on Nonlinear System Identification." International Journal of Photoenergy 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/2923731.

Full text

Abstract:

Solar photovoltaic (PV) energy sources are rapidly gaining potential growth and popularity compared to conventional fossil fuel sources. As the merging of PV systems with existing power sources increases, reliable and accurate PV system identification is essential, to address the highly nonlinear change in PV system dynamic and operational characteristics. This paper deals with the identification of a PV system characteristic with a switch-mode power converter. Measured input-output data are collected from a real PV panel to be used for the identification. The data are divided into estimation and validation sets. The identification methodology is discussed. A Hammerstein-Wiener model is identified and selected due to its suitability to best capture the PV system dynamics, and results and discussion are provided to demonstrate the accuracy of the selected model structure.

APA, Harvard, Vancouver, ISO, and other styles

39

Pszczółkowski, Józef. "Identification of the electric starting system dynamic features." AUTOBUSY – Technika, Eksploatacja, Systemy Transportowe 24, no. 6 (June 30, 2019): 248–56. http://dx.doi.org/10.24136/atest.2019.159.

Full text

Abstract:

The article describes the starting systems of combustion engines and the conditions of their operation. Traditional stationary characteristics of the acid battery and the electric starter were discussed. Structural models of functional components of the starter system: battery and starter are presented as classic electrical circuits with typical RLC elements. The equations of structural components operation in dynamic conditions were formulated and their solutions and characteristics were presented. The results of tests of the functioning of the starting system and its components, battery and starter in the test stand conditions and while driving the crankshaft of the engine, showing the behaviour and the course of work parameters under dynamic conditions are presented.

APA, Harvard, Vancouver, ISO, and other styles

40

Kawagishi, Ryohei, Daisuke Yamanaka, and Yasuyuki Shirai. "Proposal of Dynamic Modeling of Distribution System with System Identification." Journal of International Council on Electrical Engineering 4, no. 3 (July 2014): 258–64. http://dx.doi.org/10.5370/jicee.2014.4.3.258.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

KHALED and Mohamed BOUMEHRAZ. "Black-Box System Identification for Low-Cost Quadrotor Attitude at Hovering." Electrotehnica, Electronica, Automatica 70, no. 4 (November 15, 2022): 88–97. http://dx.doi.org/10.46904/eea.22.70.4.1108009.

Full text

Abstract:

The accuracy of dynamic modelling of unmanned aerial vehicles, specifically quadrotors, is gaining importance since strict conditionalities are imposed on rotorcraft control. The system identification plays a crucial role as an effective approach for the problem of the fine-tuning dynamic models for applications such control system design and as handling quality evaluation. This paper focuses on black-box identification, describing the quadrotor dynamics based on experimental setup through sensor preparation for data collection, modelling, control design, and verification stages.

APA, Harvard, Vancouver, ISO, and other styles

42

Kumar Pattanaik, Rakesh, Mihir N. Mohanty, Srikanta Ku. Mohapatra, and Binod Ku. Pattanayak. "Nonlinear Dynamic System Identification of ARX Model for Speech Signal Identification." Computer Systems Science and Engineering 46, no. 1 (2023): 195–208. http://dx.doi.org/10.32604/csse.2023.029591.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Haosheng, Chen, and Chen Darong. "Identification of a Model Helicopter’s Yaw Dynamics." Journal of Dynamic Systems, Measurement, and Control 127, no. 1 (March 1, 2005): 140–45. http://dx.doi.org/10.1115/1.1849243.

Full text

Abstract:

To identify the micro helicopter’s yaw dynamics, the system identification method is used and is proved to be suitable according to the validation results. In order to strengthen the information of the dynamics and reduce the effect of the noise when processing the experiment data, the conventional system identification method is modified and a weighted criterion is investigated to estimate the model parameters. In calculating the factors of the weighted criterion, a perceptron is trained to allocate the factors automatically. The model validation result shows that the model derived by this kind of method can fit the measured outputs well. The modified system identification method would be useful in identifying dynamic systems which use the multiexperiment data.

APA, Harvard, Vancouver, ISO, and other styles

44

M’Closkey, Robert T., Steve Gibson, and Jason Hui. "System Identification of a MEMS Gyroscope." Journal of Dynamic Systems, Measurement, and Control 123, no. 2 (June 10, 1999): 201–10. http://dx.doi.org/10.1115/1.1369360.

Full text

Abstract:

This paper reports the experimental system identification of the Jet Propulsion Laboratory MEMS vibratory rate gyroscope. A primary objective is to estimate the orientation of the stiffness matrix principal axes for important sensor dynamic modes with respect to the electrode pick-offs in the sensor. An adaptive lattice filter is initially used to identify a high-order two-input/two-output transfer function describing the input/output dynamics of the sensor. A three-mode model is then developed from the identified input/output model to determine the axes’ orientation. The identified model, which is extracted from only two seconds of input/output data, also yields the frequency split between the sensor’s modes that are exploited in detecting the rotation rate. The principal axes’ orientation and frequency split give direct insight into the source of quadrature measurement error that corrupts detection of the sensor’s angular rate.

APA, Harvard, Vancouver, ISO, and other styles

45

Woś, Piotr, and Ryszard Dindorf. "Modeling and Identification of the Hydraulic Servo Drive." EPJ Web of Conferences 213 (2019): 02100. http://dx.doi.org/10.1051/epjconf/201921302100.

Full text

Abstract:

This paper discusses possibilities to apply the dynamic identification with a discrete linear model while assessing the state of the electro-hydraulic drive dynamics. This evaluation is crucial while designing modern power or positional control systems. Experimental data is applied in order to determine the model dynamics of the real system and estimate unknown parameters of an object model. The dependencies were interpreted. The paper includes the selected results of current identification tests of the electro-hydraulic drive system as a result of which the discrete parametric object model was derived.

APA, Harvard, Vancouver, ISO, and other styles

46

Shu, Hua, and Huai Lin Shu. "Identification of Multivariable Nonlinear Dynamic System Based on PID Neural Network." Applied Mechanics and Materials 719-720 (January 2015): 475–81. http://dx.doi.org/10.4028/www.scientific.net/amm.719-720.475.

Full text

Abstract:

System identification is the basis for control system design. For linear time-invariant systems have a variety of identification methods, identification methods for nonlinear dynamic system is still in the exploratory stage. Nonlinear identification method based on neural network is a simple and effective general method that does not require too much priori experience about the system to be identified. Through training and learning, the network weights are corrected to achieve the purpose of system identification. The paper is about the identification of multivariable nonlinear dynamic system based on PID neural network. The structure and algorithm of PID neural network are introduced and the properties and characteristics are analyzed. The system identification is completed and the results are fast convergence.

APA, Harvard, Vancouver, ISO, and other styles

47

Zhou, Hongpeng, Chahine Ibrahim, Wei Xing Zheng, and Wei Pan. "Sparse Bayesian deep learning for dynamic system identification." Automatica 144 (October 2022): 110489. http://dx.doi.org/10.1016/j.automatica.2022.110489.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Deflorian, Michael, and Susanne Zaglauer. "Design of Experiments for nonlinear dynamic system identification." IFAC Proceedings Volumes 44, no. 1 (January 2011): 13179–84. http://dx.doi.org/10.3182/20110828-6-it-1002.01502.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Lardies, Joseph, and Noureddine Larbi. "Dynamic System Parameter Identification by Stochastic Realization Methods." Journal of Vibration and Control 7, no. 5 (July 2001): 711–28. http://dx.doi.org/10.1177/107754630100700506.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Peterson, S. T., D. I. McLean, and D. G. Pollock. "Application of Dynamic System Identification to Timber Bridges." Journal of Structural Engineering 129, no. 1 (January 2003): 116–24. http://dx.doi.org/10.1061/(asce)0733-9445(2003)129:1(116).

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!