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Relevant bibliographies by topics / System identification and control

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Academic literature on the topic 'System identification and control'

Author: Grafiati

Published: 4 June 2021

Last updated: 21 February 2023

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Journal articles on the topic "System identification and control"

1

Chiuso, A., and G. Pillonetto. "System Identification: A Machine Learning Perspective." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (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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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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Karshenas, A. M., M. W. Dunnigan, and B. W. Williams. "System Identification for Vibration Control." IFAC Proceedings Volumes 30, no. 6 (1997): 535–40. http://dx.doi.org/10.1016/s1474-6670(17)43419-7.

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

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

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

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

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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 (1995): 349–57. http://dx.doi.org/10.1016/0360-1323(94)00063-x.

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Dissertations / Theses on the topic "System identification and control"

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Szabo, Andrew P. "System Identification and Model-Based Control of Quadcopter UAVs." Wright State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1553197265058507.

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Li, Liangmin. "Continuous time nonlinear system identification." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.341867.

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Brunke, Shelby Scott. "Nonlinear filtering and system identification algorithms for autonomous systems /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/7095.

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Haider, Usama. "Smart Maintenance using System Identification." Thesis, Högskolan i Gävle, Avdelningen för elektroteknik, matematik och naturvetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-30735.

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This project discusses the use of System Identification for Smart Maintenance. System Identification is the process of finding a mathematical model of a system using empirical data. The mathematical model can then be used to detect and predict the maintenance needs, which is considered as Smart Maintenance. Smart maintenance strategies have gained pretty much importance recently, since it contributes to economically sustainable production. This project uses the LAVA-framework, proposed in [1] for non-linear system identification, which has the capability of explaining the dynamics of the system very well, and at the same time follows the principle of parsimony. A nominal model is first identified using data from a system that operates under normal operating conditions, then the identified nominal model is used to detect when the system starts to deviate from normal behavior, and these deviations indicate the deteriorations in the system. Furthermore, a new Multiple Model Method which is developed in [2] using the similar idea from LAVA, is applied on the large data set of a system that operates on separate batches and units, which identifies individual model for each batch and unit, which is then used to detect the deficient units or batches and changes in the system behavior. Finally, the proposed methods are applied to two different real world industrial cases; a Heat exchanger and a Wood Moulder Machine. In the first, the purpose is to detect the dirt in a Heat Exchanger, and in the second, the goal is to detect when the tool in a Wood Moulder Machine needs to be changed.

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Salam, Md Abdul, and Md Mafizul Islam. "Modelling and Control System Design to control Water temperature in Heat Pump." Thesis, Karlstads universitet, Avdelningen för fysik och elektroteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-30680.

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The thesis has been conducted at Hetvägg AB and the aim is to develop a combined PID and Model Predictive Controller (MPC) controller for an air to water heat pump system that supplies domestic hot water (DHW) to the users. The current control system is PLC based but because of its big size and expensive maintenance it must be replaced with a robust controller for the heat pump. The main goal of this project has been to find a suitable improvement strategy. By constructing a model of the system, the control system has been evaluated. First a model of the system is derived using system identification techniques in Matlab-Simulink; since the system is nonlinear and dynamic a model of the system is needed before the controller is implemented. The data has been estimated and validated for the final selection of the model in system identification toolbox and then the controller is designed for the selected model. The combined PID and MPC controller utilizes the obtained model to predict the future behavior of the system and by changing the constraints an optimal control of the system is achieved. In this thesis work, first the PID and MPC controller are evaluated and their results are compared using transient and frequency response plots. It is seen that the MPC obtained better control action than the PID controller, after some tuning the MPC controller is capable of maintaining the outlet water temperature to the reference or set point value. Both the controllers are combined to remove the minor instabilities from the system and also to obtain a better output. From the transient response behavior it is seen that the combined MPC and PID controller delivered good output response with minimal overshoot, rise time and settling time.

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Kristinsson, Kristinn. "Genetic algorithms in system identification and control." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/29628.

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Current online identification techniques are recursive and involve local search techniques. In this thesis, we show how genetic algorithms, a parallel, global search technique emulating natural genetic operators can be used to estimate the poles and zeros of a dynamical system. We also design an adaptive controller based on the estimates. The algorithms are shown to be useful for continuous time parameter identifications and to be able to identify directly physical parameters of a system. Simulations and an experiment show the technique to be satisfactory and to provide unbiased estimates in presence of colored noise.<br>Applied Science, Faculty of<br>Electrical and Computer Engineering, Department of<br>Graduate

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Tayamon, Soma. "Nonlinear system identification with applications to selective catalytic reduction systems." Licentiate thesis, Uppsala universitet, Avdelningen för systemteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-186963.

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The stringent regulations on the emissions levels of heavy duty vehicles create a demand for new methods of reducing harmful emissions from the engine. In order to be able to follow these increasingly stricter legislations, complex aftertreatment systems are used. Achievement of optimal performance of these systems requires accurate models that can be used for control design. As a result, the interest in modelling and control of aftertreatment systems has increased. This thesis deals with the modelling of the nitrogen oxide (NOx) emissions from heavy duty vehicles using the selective catalyst as an aftertreatment system for its reduction. The process of the selective catalytic reduction (SCR) is nonlinear since the chemical reactions involved are highly depending on the operating point. The momentary operating point is defined by the driving profile of the vehicle which, for example, includes cold and hot engine starts, highway and urban driving. The purpose of this thesis is to investigate different methods for nonlinear system identification of SCR systems with control in mind. The first two papers contain the theoretical work of this thesis. The first paper deals with improvement of an existing recursive prediction error method (RPEM) where a more accurate discretisation algorithm was used to improve the accuracy of the estimated nonlinear model. The second paper deals with analysis of the convergence properties of the algorithm. For this analysis several conditions were formulated that link the global and local convergence properties of the algorithm to stability properties of an associated differential equation. Global convergence to a stationary point was shown. In the third paper, the RPEM is used for identification of the SCR system and finally the fourth paper a Hammerstein–Wiener model for identification of the SCR system is applied. In both these cases the black-box models could predict the NOx behaviour of the SCR system quite well. The nonlinear models were shown to describe the SCR system more accurately than linear models.

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Liu, Xing. "System identification and prediction using neural networks." Thesis, Cardiff University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.388229.

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Lee, James X. "On fuzzy logic systems, nonlinear system identification, and adaptive control." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/nq26881.pdf.

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Lee, James X. (James Xiang) Carleton University Dissertation Engineering Mechanical and Aerospace. "On fuzzy logic systems, nonlinear system identification, and adaptive control." Ottawa, 1997.

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Books on the topic "System identification and control"

1

Fuzzy control and identification. Wiley, 2010.

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Chʻen, Han-fu. Identification and stochastic adaptive control. Birkhäuser, 1991.

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Boutalis, Yiannis, Dimitrios Theodoridis, Theodore Kottas, and Manolis A. Christodoulou. System Identification and Adaptive Control. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06364-5.

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Tøffner-Clausen, Steen. System Identification and Robust Control. Springer London, 1996. http://dx.doi.org/10.1007/978-1-4471-1513-7.

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Petre, Stoica, ed. System identification. Prentice-Hall, 1989.

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1949-, Byrnes Christopher I., Lindquist Anders, and International Symposium on the Mathematical Theory of Networks and Systems (7th : 1985 : Royal Institute of Technology, Stockholm, Sweden), eds. Modelling, identification and robust control. North-Holland, 1986.

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7

Multivariable system identification for process control. Pergamon, 2001.

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Qi, Ruiyun, Gang Tao, and Bin Jiang. Fuzzy System Identification and Adaptive Control. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19882-4.

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Wang, Liuping, and Hugues Garnier, eds. System Identification, Environmental Modelling, and Control System Design. Springer London, 2012. http://dx.doi.org/10.1007/978-0-85729-974-1.

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Sung, Su Whan. Process identification and PID control. Wiley, 2009.

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Book chapters on the topic "System identification and control"

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Davis, M. H. A., and R. B. Vinter. "System identification." In Stochastic Modelling and Control. Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-009-4828-0_4.

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Westphal, L. C. "System identification." In Sourcebook of Control Systems Engineering. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-1805-1_30.

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Westphal, Louis C. "System identification." In Handbook of Control Systems Engineering. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1533-3_30.

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Kulikov, Gennady G., and Haydn A. Thompson. "Linear System Identification." In Advances in Industrial Control. Springer London, 2004. http://dx.doi.org/10.1007/978-1-4471-3796-2_5.

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Mahmoud, Magdi S., and Yuanqing Xia. "System Identification Methods." In Applied Control Systems Design. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2879-3_3.

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Tøffner-Clausen, Steen. "Classical System Identification." In Advances in Industrial Control. Springer London, 1996. http://dx.doi.org/10.1007/978-1-4471-1513-7_9.

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Gawronski, Wodek. "Balanced system identification." In Balanced Control of Flexible Structures. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3540760172_6.

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Ninness, Brett. "System Identification Software." In Encyclopedia of Systems and Control. Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-5058-9_105.

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Ninness, Brett. "System Identification Software." In Encyclopedia of Systems and Control. Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5102-9_105-1.

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Ninness, Brett. "System Identification Software." In Encyclopedia of Systems and Control. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-44184-5_105.

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Conference papers on the topic "System identification and control"

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Jameson, Pierre-Daniel, and Alastair Cooke. "Developing real-time system identification for UAVs." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334761.

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Haider, Sajjad, Samad Hassan, and Mohammad Nishat. "Influence Nets based Decision Support System." In Modelling, Identification and Control. ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.770-015.

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Valero, Carlos E., and Gustavo Sanchez. "A New Glucose Regulation System Model." In Modelling, Identification and Control. ACTAPRESS, 2014. http://dx.doi.org/10.2316/p.2014.809-061.

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Patcharaprakiti, Nopporn, Jatturit Thongprong, Krissanapong Kirtikara, and Jeerawan Saelao. "Linear System Analysis and State Observer Design of Grid Connected Inverter Model based on System Identification." In Modelling, Identification and Control. ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.769-027.

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Awaji, Keiichiro, Yutaro Watanabe, and Ryuya Uda. "Proposal of Motion Capturing System for Authentication." In Modelling, Identification and Control. ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.770-040.

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Soumelidis, Alexandros, József Bokor, and Ferenc Schipp. "Identification of System Poles using Hyperbolic Metrics." In Modelling, Identification and Control. ACTAPRESS, 2013. http://dx.doi.org/10.2316/p.2013.799-052.

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Kang, Dongwann, and Kyunghyun Yoon. "An Emotion-based Image Color Modification System." In Modelling, Identification and Control. ACTAPRESS, 2017. http://dx.doi.org/10.2316/p.2017.848-056.

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Kubalčík, M., and V. Bobál. "Adaptive Predictive Control of Three - Tank - System." In Modelling, Identification, and Control. ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.675-037.

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Ahmad, Robiah, Saiful Farhan Mohd Samsuri, and Mohd Zakimi Zakaria. "Evapotranspiration Prediction using System Identification and Genetic Algorithm." In Modelling, Identification and Control. ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.769-053.

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Aloul, Fadi, Assim Sagahyroon, Ali Nahle, Makram Abou Dehn, and Raneem Al Anani. "GuideME: An Effective RFID-based Traffic Monitoring System." In Modelling, Identification and Control. ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.770-036.

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Reports on the topic "System identification and control"

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Dohner, J. L. System identification for robust control design. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/72725.

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Zhou, Kemin, and Guoxiang Gu. Robust System Identification and Control Design. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada392562.

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Pearson, Allan E. Control and Identification of Time Varying Systems. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada177567.

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Pearson, A. E. Control and Identification of Time Varying Systems. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada159067.

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Gibson, Steve, and Tsu-Chin Tsao. Control, Filtering and System Identification for High Energy Lasers and Laser Communications. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada565747.

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McCallum, Marvin C., Alvah C. Bittner, Badalamente Jr., and Richard V. Empirical Identification of User Information Requirements in Command and Control System Evaluation. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada237093.

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Georgiou, Tryphon T. Aspects of Modeling, Identification and Control of Dynamical Systems. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada299411.

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Khorrami, F., S. U. Pillai, and S. Nourbakhsh. Modeling, Identification, and Control Design for a Flexible Pointing System with Embedded Smart Materials. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada328831.

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Basar, Tamer. Performance-Driven Robust Identification and Control of Uncertain Dynamical Systems. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/900284.

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Burns, John A., Eugene M. Cliff, and Lizette Zietsman. Computational Methods for Identification, Optimization and Control of PDE Systems. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada523367.

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