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Relevant bibliographies by topics / System Linearization

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Academic literature on the topic 'System Linearization'

Author: Grafiati

Published: 28 June 2021

Last updated: 14 February 2022

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Journal articles on the topic "System Linearization"

1

Brzózka, Jerzy. "Design and Analysis of Model Following Control Structure with Nonlinear Plant." Solid State Phenomena 180 (November 2011): 3–10. http://dx.doi.org/10.4028/www.scientific.net/ssp.180.3.

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Abstract. Linearization methods of the object: input-state and input-output linearization are used usually in a standard feedback control system. However, these systems are sensitive to the changes of nonlinear characteristics of the plant. These changes can be compensated in two types of control systems: in the model following control (MFC) and adaptive. The article presents the first solution and contains: miscellaneous structures of linear control systems with model following, brief description of the linearization’s methods, simulation example of the course control of vessel and the advantages of this solution.

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Chao, Kao-Shing Hwang, Horng-Jen. "REINFORCEMENT LINEARIZATION CONTROL SYSTEM." Cybernetics and Systems 31, no. 1 (January 2000): 115–35. http://dx.doi.org/10.1080/019697200124946.

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Cardoso, Gildeberto S., and Leizer Schnitman. "Analysis of Exact Linearization and Aproximate Feedback Linearization Techniques." Mathematical Problems in Engineering 2011 (2011): 1–17. http://dx.doi.org/10.1155/2011/205939.

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This paper presents a study of linear control systems based on exact feedback linearization and approximate feedback linearization. As exact feedback linearization is applied, a linear controller can perform the control objectives. The approximate feedback linearization is required when a nonlinear system presents a noninvolutive property. It uses a Taylor series expansion in order to compute a nonlinear transformation of coordinates to satisfy the involutivity conditions.

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Li, Chunbiao, Julien Clinton Sprott, and Wesley Thio. "Linearization of the Lorenz system." Physics Letters A 379, no. 10-11 (May 2015): 888–93. http://dx.doi.org/10.1016/j.physleta.2015.01.003.

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Zhang, Bin, and Yung C. Shin. "A Data-Driven Approach of Takagi-Sugeno Fuzzy Control of Unknown Nonlinear Systems." Applied Sciences 11, no. 1 (December 23, 2020): 62. http://dx.doi.org/10.3390/app11010062.

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A novel approach to build a Takagi-Sugeno (T-S) fuzzy model of an unknown nonlinear system from experimental data is presented in the paper. The neuro-fuzzy models or, more specifically, fuzzy basis function networks (FBFNs) are trained from input–output data to approximate the nonlinear systems for which analytical mathematical models are not available. Then, the T-S fuzzy models are derived from the direct linearization of the neuro-fuzzy models. The operating points for linearization are chosen using the evolutionary strategy to minimize the global approximation error so that the T-S fuzzy models can closely approximate the original unknown nonlinear system with a reduced number of linearizations. Based on T-S fuzzy models, optimal controllers are designed and implemented for a nonlinear two-link flexible joint robot, which demonstrates the possibility of implementing the well-established model-based optimal control method onto unknown nonlinear dynamic systems.

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Ayalur Krishnamoorthy, Parvathy, Kamaraj Vijayarajan, and Devanathan Rajagopalan. "Generalized Quadratic Linearization of Machine Models." Journal of Control Science and Engineering 2011 (2011): 1–10. http://dx.doi.org/10.1155/2011/926712.

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In the exact linearization of involutive nonlinear system models, the issue of singularity needs to be addressed in practical applications. The approximate linearization technique due to Krener, based on Taylor series expansion, apart from being applicable to noninvolutive systems, allows the singularity issue to be circumvented. But approximate linearization, while removing terms up to certain order, also introduces terms of higher order than those removed into the system. To overcome this problem, in the case of quadratic linearization, a new concept called “generalized quadratic linearization” is introduced in this paper, which seeks to remove quadratic terms without introducing third- and higher-order terms into the system. Also, solution of generalized quadratic linearization of a class of control affine systems is derived. Two machine models are shown to belong to this class and are reduced to only linear terms through coordinate and state feedback. The result is applicable to other machine models as well.

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Lenz, Henning, and Dragan Obradovic. "Robust Control of the Chaotic Lorenz System." International Journal of Bifurcation and Chaos 07, no. 12 (December 1997): 2847–54. http://dx.doi.org/10.1142/s0218127497001928.

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This paper presents a global approach for controlling the Lorenz system. The basic idea is to partially cancel the nonlinear cross-coupling terms such that the stability of the resulting system can be guaranteed by sequentially proving the stability of each individual state. The method combines ideas from feedback linearization, classical control theory, and Lyapunov's second method. Robust behavior with respect to model uncertainties in the feedback loop is proven. The performance of partial linearization compared to input-state linearization is illustrated on tracking of several trajectories including a periodic orbit and a steady state.

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Lin, Faxing. "Hartman’s linearization on nonautonomous unbounded system." Nonlinear Analysis: Theory, Methods & Applications 66, no. 1 (January 2007): 38–50. http://dx.doi.org/10.1016/j.na.2005.11.007.

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Diallo, Amadou, and R. Joel Rahn. "Direct linearization of system dynamics models." System Dynamics Review 6, no. 2 (1990): 214–18. http://dx.doi.org/10.1002/sdr.4260060207.

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Yong-guang, Yu, and Zhang Suo-chun. "Controlling lü-system using partial linearization." Applied Mathematics and Mechanics 25, no. 12 (December 2004): 1437–42. http://dx.doi.org/10.1007/bf02438302.

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Dissertations / Theses on the topic "System Linearization"

1

Lee, Jungkun. "Optimal linearization of anharmonic oscillators /." Online version of thesis, 1991. http://hdl.handle.net/1850/11021.

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Liu, Yong. "NEURAL ADAPTIVE NONLINEAR TRACKING USING TRAJECTORY LINEARIZATION." Ohio University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1177092159.

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Yang, Bo. "Output Feedback Control of Nonlinear Systems with Unstabilizable/Undetectable Linearization." Case Western Reserve University School of Graduate Studies / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=case1132634014.

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Maeda, Ken. "Nonlinear control system of inverted pendulum based on input-output linearization." Diss., Online access via UMI:, 2006.

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Han, JeongHeon. "An LMI approach to stochastic linear system design using alternating linearization /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2005. http://wwwlib.umi.com/cr/ucsd/fullcit?p3184209.

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Huang, Rui. "OUTPUT FEEDBACK TRACKING CONTROL OF NONLINEAR TIME-VARYING SYSTEMS BY TRAJECTORY LINEARIZATION." Ohio University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1178906759.

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Eriksson, Marcus. "Analysis of Digital Predistortion in a Wideband Arbitrary Waveform Generator." Thesis, Linköpings universitet, Kommunikationssystem, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-123410.

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Digital predistortion is a signal processing technique used to remove undesired distortions caused by nonlinear system effects. This method is predominately used to linearize power amplifiers in communication systems in order to achieve efficient transmitter circuits. However, the technique can readily be applied to cancel undesired nonlinear behavior in other types of systems. This thesis investigates the effectiveness of digital predistortion in the context of a wideband arbitrary waveform generator. A theoretical foundation discussing nonlinear system models, predistortion architectures and system identification methods is complemented with a simulation study and followed by verification on a real system. The best predistorter is able to fully suppress the undesired distortions for any fixed two-tone sinusoidal signal. Furthermore, the results indicate the existence of a wideband predistorter which yield acceptable suppression over a frequency range of several hundred MHz.
Digital predistorsion är en signalbehandlingsteknik som används för att undertrycka oönskade distorsioner orsakade av icke-linjära effekter i elektriska system. Denna metod används i huvudsak för att linjärisera effektförstärkare i kommunikationssystem för att erhålla effektiva sändarkedjor men tekniken kan utan större problem även tillämpas på andra typer av icke-linjära system. Denna uppsats undersöker i vilken utsräckning digital predistorsion kan användas för att undertrycka oönskade signaldistorsioner i en bredbandig signalgenerator. Uppsatsen presenterar en bakgrund som utgår ifrån teorin om icke-linjära systemmodeller, arkiteturer för predistorsion och systemidentifieringsmetoder. En kvantitativ studie i en simuleringsmiljö åtföjs av en utvärdering på ett verkligt system. Det bästa predistorsionssystemet åstadkommer en fullständig linjärisering i testfallet med en fix tvåtonssignal. Resultaten indikerar även att det existerar ett system som linjäriserar signaler i ett frekvensområde som uppgår till hundratals MHz.

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Gettman, Chang-Ching Lo. "Multivariable control of the space shuttle remote manipulator system using linearization by state feedback." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/46419.

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Lindahl, Karl-Olof. "On the linearization of non-Archimedean holomorphic functions near an indifferent fixed point." Doctoral thesis, Växjö : Växjö University Press, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-1713.

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Rothman, Keith Eric. "Validation of Linearized Flight Models using Automated System-Identification." DigitalCommons@CalPoly, 2009. https://digitalcommons.calpoly.edu/theses/81.

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Optimization based flight control design tools depend on automatic linearization tools, such as Simulink®’s LINMOD, to extract linear models. In order to ensure the usefulness and correctness of the generated linear model, this linearization must be accurate. So a method of independently verifying the linearized model is needed. This thesis covers the automation of a system identification tool, CIFER®, for use as a verification tool integrated with CONDUIT®, an optimization based design tool. Several test cases are built up to demonstrate the accuracy of the verification tool with respect to analytical results and matches with LINMOD. Several common nonlinearities are tested, comparing the results from CIFER and LINMOD, as well as analytical results where possible. The CIFER results show excellent agreement with analytical results. LINMOD treated most nonlinearity as a unit gain, but some nonlinearities linearized to a zero, causing the linearized model to omit that path. Although these effects are documented within Simulink, their presence may be missed by a user. The verification tool is successful in identifying these problems when present. A section is dedicated to the diagnosis of linearization errors, suggesting solutions where possible.

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Books on the topic "System Linearization"

1

Shoikhet, David, and Mark Elin. Linearization Models for Complex Dynamical Systems. Basel: Birkhäuser Basel, 2010. http://dx.doi.org/10.1007/978-3-0346-0509-0.

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Socha, Leslaw. Linearization Methods for Stochastic Dynamic Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-72997-6.

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-H, Steeb W., ed. Nonlinear dynamical systems and Carleman linearization. Singapore: World Scientific, 1991.

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4

Pshenichnyj, Boris N. The Linearization Method for Constrained Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994.

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5

Minhas, Rajinderjeet Singh. Adaptive feedback linearization of systems with non-lipschitz nonlinearities. Ottawa: National Library of Canada, 1996.

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6

Dzielski, John Edward. A feedback linearization approach to spacecraft control using momentum exchange devices. Cambridge, Mass: The Charles Stark Draper Laboratory, 1988.

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7

El-Khatib, Ziad. Distributed CMOS bidirectional amplifiers: Broadbanding and linearization techniques. New York: Springer, 2012.

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8

Graham, Ronald E. Linearization of digital derived rate algorithm for use in linear stability analysis. [Washington, DC?]: National Aeronautics and Space Administration, 1985.

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9

Kirchgraber, Urs. Geometry in the neighborhood of invariant manifolds of maps and flows and linearization. Harlow, Essex, England: Longman Scientific & Technical, 1990.

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10

Kirchgraber, U. Geometry in the neighborhood of invariant manifolds of maps and flows and linearization. Harlow: Longman Scientific & Technical, 1990.

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Book chapters on the topic "System Linearization"

1

Kolk, W. Richard, and Robert A. Lerman. "Linearization." In Nonlinear System Dynamics, 61–93. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4684-6494-8_4.

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Chechurin, Leonid, and Sergej Chechurin. "Nonlinear System Oscillations: Harmonic Linearization Method." In Physical Fundamentals of Oscillations, 65–81. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-75154-2_7.

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Härdin, Hanna M., and Jan H. van Schuppen. "System Reduction of Nonlinear Positive Systems by Linearization and Truncation." In Positive Systems, 431–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-34774-7_55.

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Pachare, Ashwini, and Archana Thosar. "Level Control of Quadruple Tank System with Feedback Linearization." In Lecture Notes in Electrical Engineering, 455–69. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0336-5_38.

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Osypiuk, Rafał. "Indirect Linearization Concept through the Forward Model-Based Control System." In Robot Motion and Control 2011, 183–92. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2343-9_15.

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Ozaki, Tohru. "The Local Linearization Filter with Application to Nonlinear System Identifications." In Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach, 217–40. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-0854-6_10.

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Zhao, Yue, Shuguo Pan, and Yanheng Wang. "Linearization Error Analysis of Observation Equations in Pseudo Satellite Positioning System." In Lecture Notes in Electrical Engineering, 253–65. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7751-8_26.

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Bociu, Lorena, and Jean-Paul Zolésio. "Linearization of a Coupled System of Nonlinear Elasticity and Viscous Fluid." In Modern Aspects of the Theory of Partial Differential Equations, 93–120. Basel: Springer Basel, 2011. http://dx.doi.org/10.1007/978-3-0348-0069-3_6.

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Ferrándiz-Leal, J. "Linearization in Special Cases of the Perturbed Two-Body Problem." In Stability of the Solar System and Its Minor Natural and Artificial Bodies, 381. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-009-5398-7_39.

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Lee, Jong-Yong, Kye-dong Jung, BongHwa Hong, and Seongsoo Cho. "Method of Extended Input/Output Linearization for the Time-Varying Nonlinear System." In Lecture Notes in Electrical Engineering, 37–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55038-6_6.

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Conference papers on the topic "System Linearization"

1

Silva, M. P. S., A. A. Mello, F. G. Pinal, L. S. Ribeiro, J. S. Lima, and M. Silveira. "Adaptive linearization digital signals: I and Q [HDTV system PA linearization applications]." In IEEE Antennas and Propagation Society Symposium, 2004. IEEE, 2004. http://dx.doi.org/10.1109/aps.2004.1330490.

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Teodorescu, Horia-Nicolai L. "Fuzzy logic system linearization for sensors." In 2017 International Symposium on Signals, Circuits and Systems (ISSCS). IEEE, 2017. http://dx.doi.org/10.1109/isscs.2017.8034892.

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He, Tao, and Gabor C. Temes. "System-level noise filtering and linearization." In 2018 IEEE Custom Integrated Circuits Conference (CICC). IEEE, 2018. http://dx.doi.org/10.1109/cicc.2018.8357015.

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Lyu, Yibo, Xu Li, Weiwei Zhang, and Tianxiang Wang. "Digital Linearization for WDM-RoF System." In 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring). IEEE, 2021. http://dx.doi.org/10.1109/vtc2021-spring51267.2021.9448651.

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Lee, Taekyung, and Hyo-Sung Ahn. "Consensus of nonlinear system using feedback linearization." In 2010 IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA). IEEE, 2010. http://dx.doi.org/10.1109/mesa.2010.5552029.

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Gudkova, Natalya V., Vladimir M. Chuykov, and Ksenya V. Besklubova. "Digital adaptive system of power amplifier linearization." In 2015 IEEE East-West Design & Test Symposium (EWDTS). IEEE, 2015. http://dx.doi.org/10.1109/ewdts.2015.7493100.

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"Feedback linearization application for LLRF control system." In Proceedings of the 1999 American Control Conference. IEEE, 1999. http://dx.doi.org/10.1109/acc.1999.786288.

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Hai-Long Pei and Qi-Jie Zhou. "Approximate nonlinear system linearization with neural networks." In Proceedings of 16th American CONTROL Conference. IEEE, 1997. http://dx.doi.org/10.1109/acc.1997.611918.

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Michaelsen, Jorgen Andreas, and Dag T. Wisland. "A VCO linearization system for ADC applications." In 2013 IEEE 11th International New Circuits and Systems Conference (NEWCAS). IEEE, 2013. http://dx.doi.org/10.1109/newcas.2013.6573623.

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De La Torre, G., and E. A. Theodorou. "Stochastic Variational Integrators for System Propagation and Linearization." In IMA Conference on Mathematics of Robotics. Institute of Mathematics and its Applications, 2015. http://dx.doi.org/10.19124/ima.2015.001.17.

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Reports on the topic "System Linearization"

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Kokotovic, Peter V. Nonlinear System Design: Adaptive Feedback Linearization with Unmodeled Dynamics. Fort Belvoir, VA: Defense Technical Information Center, September 1991. http://dx.doi.org/10.21236/ada248484.

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Kokotovic, Petar V. Nonlinear System Design: Adaptive Feedback Linearization with Unmodeled Dynamics. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada261360.

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Jonkman, Jason, Alan D. Wright, Gregory Hayman, and Amy N. Robertson. Full-System Linearization for Floating Offshore Wind Turbines in OpenFAST: Preprint. Office of Scientific and Technical Information (OSTI), December 2018. http://dx.doi.org/10.2172/1489323.

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Kim, Taihyun, and Eyad H. Abed. Stationary Bifurcation Control for Systems with Uncontrollable Linearization. Fort Belvoir, VA: Defense Technical Information Center, January 1999. http://dx.doi.org/10.21236/ada438515.

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Wang, Jianliang, and Wilson J. Rugh. On the Pseudo-Linearization Problem for Nonlinear Systems. Fort Belvoir, VA: Defense Technical Information Center, March 1988. http://dx.doi.org/10.21236/ada194021.

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