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Journal articles on the topic 'System identification and control'

Author: Grafiati

Published: 4 June 2021

Last updated: 21 February 2023

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1

Chiuso, A., and G. Pillonetto. "System Identification: A Machine Learning Perspective." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (May 3, 2019): 281–304. http://dx.doi.org/10.1146/annurev-control-053018-023744.

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Estimation of functions from sparse and noisy data is a central theme in machine learning. In the last few years, many algorithms have been developed that exploit Tikhonov regularization theory and reproducing kernel Hilbert spaces. These are the so-called kernel-based methods, which include powerful approaches like regularization networks, support vector machines, and Gaussian regression. Recently, these techniques have also gained popularity in the system identification community. In both linear and nonlinear settings, kernels that incorporate information on dynamic systems, such as the smoothness and stability of the input–output map, can challenge consolidated approaches based on parametric model structures. In the classical parametric setting, the complexity of the model (the model order) needs to be chosen, typically from a finite family of alternatives, by trading bias and variance. This (discrete) model order selection step may be critical, especially when the true model does not belong to the model class. In regularization-based approaches, model complexity is controlled by tuning (continuous) regularization parameters, making the model selection step more robust. In this article, we review these new kernel-based system identification approaches and discuss extensions based on nuclear and [Formula: see text] norms.

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2

Faudzi, A. A. M., N. H. I. M. Lazim, K. Suzumori, and M. Azizir-Rahim Mukri. "System Identification and PID-PSO Force Control of Thin Soft Actuator." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2015.6 (2015): 349–50. http://dx.doi.org/10.1299/jsmeicam.2015.6.349.

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3

Karshenas, A. M., M. W. Dunnigan, and B. W. Williams. "System Identification for Vibration Control." IFAC Proceedings Volumes 30, no. 6 (May 1997): 535–40. http://dx.doi.org/10.1016/s1474-6670(17)43419-7.

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4

Mulder, J. A. "Aircraft Flight Control System Identification." IFAC Proceedings Volumes 21, no. 9 (August 1988): 1327–32. http://dx.doi.org/10.1016/s1474-6670(17)54913-7.

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5

Owens, D. H., and L. Wang. "System Identification and Approximation in Control Systems Design." IFAC Proceedings Volumes 18, no. 5 (July 1985): 897–902. http://dx.doi.org/10.1016/s1474-6670(17)60675-x.

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6

Li, Zhixiong. "Robust global sliding model control for water-hull-propulsion unit interaction systems - Part 1: System boundary identification." Tehnicki vjesnik - Technical Gazette 22, no. 1 (2015): 209–15. http://dx.doi.org/10.17559/tv-20141208054126.

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7

Bihl, Trevor J., Jerrel R. Mitchell, and R. Dennis Irwin. "Hybrid System Identification for MIMO Control-System Design." IFAC Proceedings Volumes 46, no. 19 (2013): 411–16. http://dx.doi.org/10.3182/20130902-5-de-2040.00023.

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8

Yang, X. S., R. R. Mohler, and Z. H. Farooqi. "Immune Control System Modelling and Identification." IFAC Proceedings Volumes 20, no. 5 (July 1987): 61–66. http://dx.doi.org/10.1016/s1474-6670(17)55243-x.

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9

Weyer, Erik, Geoff Bell, and Peter L. Lee. "System identification for generic model control." Journal of Process Control 9, no. 4 (August 1999): 357–64. http://dx.doi.org/10.1016/s0959-1524(98)00050-x.

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10

T.P.So, Albert, W. L. Chan, T. T. Chow, and W. L. Tse. "New HVAC control by system identification." Building and Environment 30, no. 3 (July 1995): 349–57. http://dx.doi.org/10.1016/0360-1323(94)00063-x.

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11

Giraldo, Diego, Osamu Yoshida, Shirley J. Dyke, and Luca Giacosa. "Control-oriented system identification using ERA." Structural Control and Health Monitoring 11, no. 4 (2004): 311–26. http://dx.doi.org/10.1002/stc.46.

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12

Natke, H. G. "System identification." Automatica 28, no. 5 (September 1992): 1069–71. http://dx.doi.org/10.1016/0005-1098(92)90167-e.

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13

Fujiwara, Ryoichi, Shigeru Abe, Hirosuke Doi, and Hiroyuki Tanaka. "Parameter identification of frequency control system in power systems." IEEJ Transactions on Power and Energy 105, no. 1 (1985): 1–6. http://dx.doi.org/10.1541/ieejpes1972.105.1.

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14

Hietpas, Steven M., and Donald A. Pierre. "System Identification Using Prony Methods For Digital Control Systems." IFAC Proceedings Volumes 27, no. 8 (July 1994): 903–8. http://dx.doi.org/10.1016/s1474-6670(17)47824-4.

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15

Ohnishi, K., N. Matsui, and Y. Hori. "Estimation, identification, and sensorless control in motion control system." Proceedings of the IEEE 82, no. 8 (1994): 1253–65. http://dx.doi.org/10.1109/5.301687.

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16

Sugiyama, N. "System Identification of Jet Engines." Journal of Engineering for Gas Turbines and Power 122, no. 1 (October 20, 1999): 19–26. http://dx.doi.org/10.1115/1.483172.

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System identification plays an important role in advanced control systems for jet engines, in which controls are performed adaptively using data from the actual engine and the identified engine. An identification technique for jet engine using the Constant Gain Extended Kalman Filter (CGEKF) is described. The filter is constructed for a two-spool turbofan engine. The CGEKF filter developed here can recognize parameter change in engine components and estimate unmeasurable variables over whole flight conditions. These capabilities are useful for an advanced Full Authority Digital Electric Control (FADEC). Effects of measurement noise and bias, effects of operating point and unpredicted performance change are discussed. Some experimental results using the actual engine are shown to evaluate the effectiveness of CGEKF filter. [S0742-4795(00)00401-4]

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17

Carbone, Paolo, Johan Schoukens, and Alessio De Angelis. "One-bit System Identification." IFAC-PapersOnLine 54, no. 7 (2021): 571–76. http://dx.doi.org/10.1016/j.ifacol.2021.08.421.

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18

Cho, Yun Hyun, and Hoon Heo. "System identification technique for control of hybrid bio-system." Journal of Mechanical Science and Technology 33, no. 12 (December 2019): 6045–51. http://dx.doi.org/10.1007/s12206-019-1148-6.

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19

Tang, Yu Dong, Xiao Chun Zhu, Lu Feng, and Chen Gui. "Study on System Identification Method Based on PID Control Algorithm." Advanced Materials Research 383-390 (November 2011): 7644–48. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.7644.

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Parameter identification method is researched for the single-input and single-output systems in this paper. Based on the analysis of method of least squares, the new system identification method based on PID control algorithm is proposed. The PID controller is established by connecting the input and output with the system error and the system parameter. And the results of the simulations state that the new identification method is feasible and effective.System identification is the most important part in modern control theory. The definition of system identification proposed by L. A. Zadeh is that, system identification is determination the system in the gained systems which equivalent to the unknown system, according to the input and output of the unknown system. Traditional identification methods include impulse response, least square and maximum likelihood [1-2]. Identification speed and identification precise are all needed in the system identification. But identification speed is incompatible with identification precision. The improvement of identification do always conduce to the increase of identification precision, it can be verified by the identification examples, which is identified by the traditional identification methods. So for higher identification precision, the identification speed is lower. Now, a lot of high precision identification methods based on neural network or genetic algorithm, is proposed [3-7]. These new identification methods can gain very high identification precision, but it needs too much time. A new system identification method based on PID controller is proposed in this paper. It states by simulation that the new me-thod can improve the identification precision and speed.

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20

So¨derstro¨m, T., P. Stoica, and Rolf Johansson. "System Identification." Journal of Dynamic Systems, Measurement, and Control 115, no. 4 (December 1, 1993): 739–40. http://dx.doi.org/10.1115/1.2899207.

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21

Formentin, Simone, and Alessandro Chiuso. "Control-oriented regularization for linear system identification." Automatica 127 (May 2021): 109539. http://dx.doi.org/10.1016/j.automatica.2021.109539.

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22

Colón, Diego, Átila Madureira Bueno, Yuri Smiljanic Andrade, Ivando Severino Diniz, and José Manoel Balthazar. "Nonlinear Ball and Beam Control System Identification." Applied Mechanics and Materials 706 (December 2014): 69–80. http://dx.doi.org/10.4028/www.scientific.net/amm.706.69.

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The Ball and Beam system is a common didactical experiment in control laboratories that can be used to illustrate many different closed-loop control techniques. The plant itself is subjected to many nonlinear effects, which the most common comes from the relative motion between the ball and the beam. The modeling process normally uses the lagrangean formulation. However, many other nonlinear effects, such as non-viscous friction, beam flexibility, ball slip, actuator elasticity, collisions at the end of the beam, to name a few, are present. Besides that, the system is naturally unstable. In this work, we analyze a subset of these characteristics, in which the ball rolls with slipping and the friction force between the ball and the beam is non-viscous (Coulomb friction). Also, we consider collisions at the ends of the beam, the actuator consists of a (rubber made) belt attached at the free ends of the beam and connected to a DC motor. The model becomes, with those nonlinearities, a differential inclusion system. The elastic coefficients of the belt are experimentally identified, as well as the collision coefficients. The nonlinear behavior of the system is studied and a control strategy is proposed.

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23

Kristinsson, K., and G. A. Dumont. "System identification and control using genetic algorithms." IEEE Transactions on Systems, Man, and Cybernetics 22, no. 5 (1992): 1033–46. http://dx.doi.org/10.1109/21.179842.

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24

Pandey, Saurabh, and Somanath Majhi. "System Identification Under Relay and PI Control." IEEE Transactions on Circuits and Systems II: Express Briefs 67, no. 6 (June 2020): 1089–93. http://dx.doi.org/10.1109/tcsii.2019.2927302.

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25

Chandler, P. R., M. Pachter, and M. Mears. "System identification for adaptive and reconfigurable control." Journal of Guidance, Control, and Dynamics 18, no. 3 (May 1995): 516–24. http://dx.doi.org/10.2514/3.21417.

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26

Pasik-Duncan, Bozenna. "Control-oriented system identification: An H∞ approach." Automatica 38, no. 10 (October 2002): 1827–28. http://dx.doi.org/10.1016/s0005-1098(02)00068-7.

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27

Kamata, Yutaka, Hidekazu Nishimura, and Hidekuni Iida. "System Identification and Attitude Control of Motorcycle." IFAC Proceedings Volumes 36, no. 14 (August 2003): 287–92. http://dx.doi.org/10.1016/s1474-6670(17)32434-5.

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28

Kraus, Franta, Xiaobing Qiu, and Walter Schaufelberger. "Identification and Control of a Servo System." IFAC Proceedings Volumes 30, no. 11 (July 1997): 197–202. http://dx.doi.org/10.1016/s1474-6670(17)42846-1.

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29

Turetsky, Vladimir Ya, and Ekaterina S. Lemsh. "A Feedback Control in Linear System Identification." IFAC Proceedings Volumes 29, no. 8 (December 1996): 1–4. http://dx.doi.org/10.1016/s1474-6670(17)43667-6.

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30

Frewer, Michael. "Bus Identification, Communication and Control System (BICCS)." Journal of Navigation 47, no. 2 (May 1994): 141–45. http://dx.doi.org/10.1017/s0373463300012054.

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What's in a name? Well quite a lot really, and first of all, an explanation is required of our company name. PMT Limited is the trading name used for our core business area of North Staffordshire. The original full title was ‘The Potteries Motor Traction Company Limited’ but it is the abbreviation to PMT by which we have been known and loved for many decades – and who are we to argue with our customers? Under the terms of the 1985 Transport Act, we became one of the early management buyouts from the government-owned National Bus Company and chose to call the new company just PMT Limited.

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31

van den Boom, T., M. Klompstra, and A. Damen. "System Identification for H ∞ -Robust Control Design." IFAC Proceedings Volumes 24, no. 3 (July 1991): 971–76. http://dx.doi.org/10.1016/s1474-6670(17)52475-1.

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32

Sureshbabu, N., and J. A. Farrell. "Wavelet-based system identification for nonlinear control." IEEE Transactions on Automatic Control 44, no. 2 (1999): 412–17. http://dx.doi.org/10.1109/9.746278.

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33

Kurup, Nithin. "Car Theft Identification, Tracking and Control System." IOSR Journal of Computer Engineering 4, no. 2 (2012): 31–34. http://dx.doi.org/10.9790/0661-0423134.

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34

Fujiwara, Ryoichi, Shigeru Abe, Hirosuke Doi, and Hiroyuki Tanaka. "Parameter identification of load frequency control system." Electrical Engineering in Japan 105, no. 1 (1985): 99–105. http://dx.doi.org/10.1002/eej.4391050112.

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35

Doi, Motonori, Qian Chen, Ayumu Matani, Osamu Oshiro, Kosuke Sato, and Kunihiro Chihara. "Lock control system based on face identification." Systems and Computers in Japan 28, no. 13 (November 30, 1997): 1–7. http://dx.doi.org/10.1002/(sici)1520-684x(19971130)28:13<1::aid-scj1>3.0.co;2-t.

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36

Mahmoud, Magdi S., and Mirza H. Baig. "System Identification and Control Design of Vapor Compression Cycle Systems." Journal of Dynamic Systems, Measurement, and Control 136, no. 5 (May 19, 2014): 051003. http://dx.doi.org/10.1115/1.4027086.

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37

Chen, Han-Fu. "Theory of System Identification and Adaptive Control for Stochastic Systems *." IFAC Proceedings Volumes 21, no. 9 (August 1988): 51–61. http://dx.doi.org/10.1016/s1474-6670(17)54703-5.

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38

Shao, Quan Min, and Ali Cinar. "System identification and distributed control for multi-rate sampled systems." Journal of Process Control 34 (October 2015): 1–12. http://dx.doi.org/10.1016/j.jprocont.2015.06.010.

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39

Vega, David Cortes, Serafin Ramos Paz, Fernando Ornelas-Tellez, and J. Jesus Rico-Melgoza. "System Parameters’ Identification and Optimal Tracking Control for Nonlinear Systems." IFAC-PapersOnLine 51, no. 13 (2018): 431–36. http://dx.doi.org/10.1016/j.ifacol.2018.07.324.

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40

Shao, Quan Min, and Ali Cinar. "System Identification and Distributed Control for Multi-rate Sampled Systems." IFAC Proceedings Volumes 47, no. 3 (2014): 11653–58. http://dx.doi.org/10.3182/20140824-6-za-1003.01715.

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41

Zheng, D., and K. A. Hoo. "System identification and model-based control for distributed parameter systems." Computers & Chemical Engineering 28, no. 8 (July 2004): 1361–75. http://dx.doi.org/10.1016/j.compchemeng.2003.09.035.

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42

Walter, Eric. "Nonlinear system identification." Automatica 39, no. 3 (March 2003): 564–68. http://dx.doi.org/10.1016/s0005-1098(02)00239-x.

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43

Yang, Dong Jing, Jin Wu Gao, Le Lun Jiang, and Tan Xiao. "System Identification of TEG Themostatic System." Applied Mechanics and Materials 551 (May 2014): 384–88. http://dx.doi.org/10.4028/www.scientific.net/amm.551.384.

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Thrombelastograph device (TEG) is a measuring instrument of blood viscoelastic properties during coagulation. The measuring temperature of TEG is fixed at 37oC while in some surgery cases, lower temperature surroundings may be adopted. Therefore a new type of TEG with a controllable themostatic system has been designed to mimic various temperature surroundings in surgery. In this paper, a small-sized high accuracy thermostatic system for TEG was designed and its system identification was built to facilitate the development of control strategy. ARX model was supposed to analyze the system identification of the thermostatic system by Matlab System Identification Toolbox. Residual analysis method was adopted to verify the identified model. The results showed that the simulation data of ARX model was consistence with the measured data (matching degree was about 93%). Transfer function of the system can be applied to develop its control strategy.

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44

Madhav, Manu S., and Noah J. Cowan. "The Synergy Between Neuroscience and Control Theory: The Nervous System as Inspiration for Hard Control Challenges." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (May 3, 2020): 243–67. http://dx.doi.org/10.1146/annurev-control-060117-104856.

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Here, we review the role of control theory in modeling neural control systems through a top-down analysis approach. Specifically, we examine the role of the brain and central nervous system as the controller in the organism, connected to but isolated from the rest of the animal through insulated interfaces. Though biological and engineering control systems operate on similar principles, they differ in several critical features, which makes drawing inspiration from biology for engineering controllers challenging but worthwhile. We also outline a procedure that the control theorist can use to draw inspiration from the biological controller: starting from the intact, behaving animal; designing experiments to deconstruct and model hierarchies of feedback; modifying feedback topologies; perturbing inputs and plant dynamics; using the resultant outputs to perform system identification; and tuning and validating the resultant control-theoretic model using specially engineered robophysical models.

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45

QAMMAR, HELEN K., and FARAMARZ MOSSAYEBI. "SYSTEM IDENTIFICATION AND MODEL-BASED CONTROL OF A CHAOTIC SYSTEM." International Journal of Bifurcation and Chaos 04, no. 04 (August 1994): 843–51. http://dx.doi.org/10.1142/s0218127494000605.

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In this paper the control of a hyper2chaotic system is considered to show the role of system identification techniques in developing a model for effective control of highly complex systems. An indirect adaptive control scheme is considered and it is shown that simple prediction models which cannot possibly represent the dynamics of the chaotic system lead to stable control. Furthermore, it is shown that higher dimensional prediction models which more closely represent the chaotic process dynamics lead to controlled systems with sparse and disjoint basins of attraction for the desired steady state solution. The use of highly nonlinear models also results in a complex pattern of convergence to the desired state.

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46

Jin, Lai Chong, Azrul Azim Abdullah Hashim, Salmiah Ahmad, and Nor Maniha Abdul Ghani. "System Identification and Control of Automatic Car Pedal Pressing System." Journal of Intelligent Systems and Control 1, no. 1 (October 30, 2022): 78–89. http://dx.doi.org/10.56578/jisc010108.

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This paper mainly explores the system identification and control of an automatic car pedal pressing system. Specifically, the system identification was achieved using an artificial neural network, with the help of MATLAB’s System Identification Toolbox. The proportional-integral-derivative (PID) controller and fuzzy logic controller were designed, and normalized with membership functions. These functions were scaled with a gain as a scaling factor. The controller gains were tuned by a metaheuristic algorithm named particle swarm optimization (PSO). On this basis, the two controllers were compared with a number of performance indices, including integral squared error (ISE), integral absolute error (IAE), integral time absolute error (ITAE), and mean squared error (MSE). The car pedal pressing performance was measured at different speed levels for each controller.

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47

Boeva, Vasilisa, Yuri Voskoboinikov, and Rustam Mansurov. "Non-parametric identification of thermal control system elements." Analysis and data processing systems, no. 1 (March 26, 2021): 7–20. http://dx.doi.org/10.17212/2782-2001-2021-1-7-20.

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The thermal control system “Heater-Fan-Room” is represented by three different-type interconnected simpler subsystems. In this paper, a “black-box” whose structure is not specified is used as a mathematical model of the system and subsystems due to complexity of physical processes proceeding in these subsystems. For stationary linear systems, the connection between an input and an output of the “black-box” is defined by the Volterra integral equation of the first kind with an undetermined difference kernel also known as impulse response in the automatic control theory. In such a case, it is necessary to evaluate an unknown impulse response to use the “black-box” model and formulate all subsystems and the system as a whole. This condition complicates significantly the solution search of non-parametric identification problems in the system because an output of one subsystem is an input of another subsystem, so active identification schemes are unappropriated. Formally, an impulse response evaluation is a solution of the integral equation of the first kind for its kernel by registered noise-contaminated discrete input and output values. This problem is ill-posed because of the possible solution instability (impulse response evaluation in this case) relative to measurement noises in initial data. To find a unique stable solution regularizing algorithms are used, but the specificity of the impulse response identification experiment in the “Heater-Fan-Room” system do not allow applying computational methods of these algorithms (a system of linear equations or discrete Fourier transformation). In this paper, the authors propose two specific identification algorithms for complex technical systems. In these algorithms, impulse responses are evaluated using first derivatives of identified system signals that are stably calculated by smoothing cubic splines with an original smoothing parameter algorithm. The results of the complex “Heater-Fan-Room” system modeling and identification prove the efficiency of the algorithms proposed. Acknowledgments: The reported study was funded by RFBR, project number 20-38-90041.

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48

Forrai, A., H. Funato, K. Kamiyama, and S. Hashimoto. "Structural control technology: system identification and control of flexible structures." Computing & Control Engineering Journal 12, no. 6 (December 1, 2001): 257–62. http://dx.doi.org/10.1049/cce:20010602.

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49

KIKUCHI, Makoto, and Masatake SHIRAISHI. "Automatic Identification of Control Parameters in Stance Posture Control System." Transactions of the Japan Society of Mechanical Engineers Series C 66, no. 651 (2000): 3685–89. http://dx.doi.org/10.1299/kikaic.66.3685.

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50

Ljung, Lennart, Carl Andersson, Koen Tiels, and Thomas B. Schön. "Deep Learning and System Identification." IFAC-PapersOnLine 53, no. 2 (2020): 1175–81. http://dx.doi.org/10.1016/j.ifacol.2020.12.1329.

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