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Relevant bibliographies by topics / Nonlinear control methods

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Academic literature on the topic 'Nonlinear control methods'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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Journal articles on the topic "Nonlinear control methods"

1

Conte, G., C. Moog, and A. Perdon. "Algebraic Methods for Nonlinear Control Systems." IEEE Transactions on Automatic Control 52, no. 12 (December 2007): 2395–96. http://dx.doi.org/10.1109/tac.2007.911476.

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CARMICHAEL, N., and M. D. QUINN. "Fixed-Point Methods in Nonlinear Control." IMA Journal of Mathematical Control and Information 5, no. 1 (1988): 41–67. http://dx.doi.org/10.1093/imamci/5.1.41.

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Han, Jing-Qing. "Nonlinear design methods for control systems." IFAC Proceedings Volumes 32, no. 2 (July 1999): 1531–36. http://dx.doi.org/10.1016/s1474-6670(17)56259-x.

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Hager, William W. "Multiplier Methods for Nonlinear Optimal Control." SIAM Journal on Numerical Analysis 27, no. 4 (August 1990): 1061–80. http://dx.doi.org/10.1137/0727063.

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Porubov, Alexey, and Boris Andrievsky. "Control methods for localization of nonlinear waves." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 375, no. 2088 (March 6, 2017): 20160212. http://dx.doi.org/10.1098/rsta.2016.0212.

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A general form of a distributed feedback control algorithm based on the speed–gradient method is developed. The goal of the control is to achieve nonlinear wave localization. It is shown by example of the sine-Gordon equation that the generation and further stable propagation of a localized wave solution of a single nonlinear partial differential equation may be obtained independently of the initial conditions. The developed algorithm is extended to coupled nonlinear partial differential equations to obtain consistent localized wave solutions at rather arbitrary initial conditions. This article is part of the themed issue ‘Horizons of cybernetical physics’.

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Almashaal, M. J., and A. R. Gaiduk. "METHODS COMPARISON OF NONLINEAR CONTROL SYSTEMS DESIGN." Mathematical Methods in Technologies and Technics, no. 4 (2021): 21–24. http://dx.doi.org/10.52348/2712-8873_mmtt_2021_4_21.

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Hedrick, J. Karl, and Swaminathan Gopalswamy. "Nonlinear flight control design via sliding methods." Journal of Guidance, Control, and Dynamics 13, no. 5 (September 1990): 850–58. http://dx.doi.org/10.2514/3.25411.

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Shakeel, Tanzeela, Jehangir Arshad, Mujtaba Hussain Jaffery, Ateeq Ur Rehman, Elsayed Tag Eldin, Nivin A. Ghamry, and Muhammad Shafiq. "A Comparative Study of Control Methods for X3D Quadrotor Feedback Trajectory Control." Applied Sciences 12, no. 18 (September 15, 2022): 9254. http://dx.doi.org/10.3390/app12189254.

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Unmanned aerial vehicles (UAVs), particularly quadrotor, have seen steady growth in use over the last several decades. The quadrotor is an under-actuated nonlinear system with few actuators in comparison to the degree of freedom (DOF); hence, stabilizing its attitude and positions is a significant challenge. Furthermore, the inclusion of nonlinear dynamic factors and uncertainties makes controlling its maneuverability more challenging. The purpose of this research is to design, implement, and evaluate the effectiveness of linear and nonlinear control methods for controlling an X3D quadrotor’s intended translation position and rotation angles while hovering. The dynamics of the X3D quadrotor model were implemented in Simulink. Two linear controllers, linear quadratic regulator (LQR) and proportional integral derivate (PID), and two nonlinear controllers, fuzzy controller (FC) and model reference adaptive PID Controller (MRAPC) employing the MIT rule, were devised and implemented for the response analysis. In the MATLAB Simulink Environment, the transient performance of nonlinear and linear controllers for an X3D quadrotor is examined in terms of settling time, rising time, peak time, delay time, and overshoot. Simulation results suggest that the LQR control approach is better because of its robustness and comparatively superior performance characteristics to other controllers, particularly nonlinear controllers, listed at the same operating point, as overshoot is 0.0% and other factors are minimal for the x3D quadrotor. In addition, the LQR controller is intuitive and simple to implement. In this research, all control approaches were verified to provide adequate feedback for quadrotor stability.

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Gaiduk, A. R., S. G. Kapustyan, and M. J. Almashaal. "Comparison of methods of nonlinear control systems design." Vestnik IGEU, no. 6 (December 28, 2021): 54–61. http://dx.doi.org/10.17588/2072-2672.2021.6.054-061.

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The issue of designing nonlinear control systems is a complex problem. A lot of methods are known that allow us to find a suitable control for a given nonlinear object that provides asymptotic stability of the nonlinear system equilibrium and an acceptable quality of the transient process. Many of these methods are difficult to apply in practice. Thus, comparing some of the methods in terms of simplicity of use is of great interest. Two analytical methods for the synthesis of nonlinear control systems are considered. They are the algebraic polynomial-matrix method that uses a quasilinear model, and the feedback linearization method that uses the Brunovsky model in combination with special feedbacks. A comparative analysis of the algebraic polynomial-matrix method and the feedback linearization method is carried out. It is found out that the algebraic polynomial-matrix method (APM) is much simpler than the feedback linearization method (FLM). A numerical example of designing a system that is synthesized by these methods is considered. It is found out that the system synthesized by the APM method has a region of attraction of the equilibrium position twice as large as the region of attraction of the system synthesized by the FLM method. It is reasonable to use the algebraic polynomial-matrix method with the quasilinear models in case of synthesis of control systems of objects with differentiable nonlinearities.

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Bartolini, G., E. Punta, and T. Zolezzi. "Simplex Methods for Nonlinear Uncertain Sliding-Mode Control." IEEE Transactions on Automatic Control 49, no. 6 (June 2004): 922–33. http://dx.doi.org/10.1109/tac.2004.829617.

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Dissertations / Theses on the topic "Nonlinear control methods"

1

Cho, Dong-Il. "Nonlinear control methods for automotive powertrain systems." Thesis, Massachusetts Institute of Technology, 1987. http://hdl.handle.net/1721.1/14682.

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Benouarets, Mourad. "Some design methods for linear and nonlinear controllers." Thesis, University of Sussex, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.333454.

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Huynh, Nguyen. "Digital control and monitoring methods for nonlinear processes." Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-100906-083012/.

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Dissertation (Ph.D.)--Worcester Polytechnic Institute.
Keywords: Parametric optimization; nonlinear dynamics; functional equations; chemical reaction system dynamics; time scale multiplicity; robust control; nonlinear observers; invariant manifold; process monitoring; Lyapunov stability. Includes bibliographical references (leaves 92-98).

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Verschueren, Robin [Verfasser], and Moritz [Akademischer Betreuer] Diehl. "Convex approximation methods for nonlinear model predictive control." Freiburg : Universität, 2018. http://d-nb.info/1192660641/34.

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Blanchard, Eunice Anita. "Exact penalty methods for nonlinear optimal control problems." Thesis, Curtin University, 2014. http://hdl.handle.net/20.500.11937/1805.

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Research comprised of developing solution techniques to three classes of non-standard optimal control problems, namely: optimal control problems with discontinuous objective functions arising in aquaculture operations; impulsive optimal control problems with minimum subsystem durations; optimal control problems involving dual-mode hybrid systems with state-dependent switching conditions. The numerical algorithms developed involved an exact penalty approach to transform the constrained problem into an unconstrained problem which was readily solvable by a standard optimal control software.

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Altafini, Claudio. "Geometric control methods for nonlinear systems and robotic applications." Doctoral thesis, Stockholm : Tekniska högsk, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3151.

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Nanka-Bruce, Oona. "Some computer aided design methods for nonlinear control systems." Thesis, University of Sussex, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.252934.

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Wang, Dazhong. "Polynomial level-set methods for nonlinear dynamics and control /." May be available electronically:, 2007. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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Kasnakoglu, Cosku. "Reduced order modeling, nonlinear analysis and control methods for flow control problems." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1195629380.

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Haskara, Ibrahim. "Sliding mode estimation and optimization methods in nonlinear control problems." The Ohio State University, 1999. http://rave.ohiolink.edu/etdc/view?acc_num=osu1250272986.

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Books on the topic "Nonlinear control methods"

1

Lennart, Ljung, ed. Control theory: Multivariable and nonlinear methods. London: Taylor & Francis, 2000.

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2

Conte, Giuseppe, Claude H. Moog, and Anna Maria Perdon. Algebraic Methods for Nonlinear Control Systems. London: Springer London, 2007. http://dx.doi.org/10.1007/978-1-84628-595-0.

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Glad, Torkel. Control theory: Multivariable and nonlinear methods. London: Taylor & Francis, 2000.

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4

Lu, X. Y. Differential algebraic methods in nonlinear control theory. Manchester: UMIST, 1993.

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5

Turner, Matthew C., and Declan G. Bates, eds. Mathematical Methods for Robust and Nonlinear Control. London: Springer London, 2007. http://dx.doi.org/10.1007/978-1-84800-025-4.

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6

Max-plus methods for nonlinear control and estimation. Boston: Birkhäuser, 2006.

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7

Max-plus methods for nonlinear control and estimation. Boston, MA: Birkhauser, 2005.

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8

Martínez-Guerra, Rafael, Oscar Martínez-Fuentes, and Juan Javier Montesinos-García. Algebraic and Differential Methods for Nonlinear Control Theory. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12025-2.

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9

Allgüwer, F., P. Fleming, P. Kokotovic, A. B. Kurzhanski, H. Kwakernaak, A. Rantzer, J. N. Tsitsiklis, Francesco Bullo, and Kenji Fujimoto, eds. Lagrangian and Hamiltonian Methods for Nonlinear Control 2006. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73890-9.

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10

Fliess, M., and M. Hazewinkel, eds. Algebraic and Geometric Methods in Nonlinear Control Theory. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-4706-1.

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Book chapters on the topic "Nonlinear control methods"

1

Vinagre, Blas M., Inés Tejado, and S. Hassan HosseinNia. "Nonlinear control methods." In Applications in Control, edited by Ivo Petráš, 1–28. Berlin, Boston: De Gruyter, 2019. http://dx.doi.org/10.1515/9783110571745-001.

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2

Lantos, Béla, and Lőrinc Márton. "Basic Nonlinear Control Methods." In Nonlinear Control of Vehicles and Robots, 11–80. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-122-6_2.

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Grimble, Michael J., and Paweł Majecki. "Nonlinear Estimation Methods: Polynomial Systems Approach." In Nonlinear Industrial Control Systems, 553–96. London: Springer London, 2020. http://dx.doi.org/10.1007/978-1-4471-7457-8_12.

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Wagg, David, and Simon Neild. "Approximate Methods for Analysing Nonlinear Vibrations." In Nonlinear Vibration with Control, 145–209. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10644-1_4.

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5

Westphal, Louis C. "Linearization methods for nonlinear systems." In Handbook of Control Systems Engineering, 745–806. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1533-3_33.

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6

Raković, Saša V. "Set Theoretic Methods in Model Predictive Control." In Nonlinear Model Predictive Control, 41–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01094-1_3.

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7

Kawski, Matthias. "Lie Algebraic Methods in Nonlinear Control." In Encyclopedia of Systems and Control, 631–36. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-5058-9_79.

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Krener, A. J. "Differential Geometric Methods in Nonlinear Control." In Encyclopedia of Systems and Control, 275–84. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-5058-9_80.

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Kawski, Matthias. "Lie Algebraic Methods in Nonlinear Control." In Encyclopedia of Systems and Control, 1–7. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5102-9_79-1.

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Krener, A. J. "Differential Geometric Methods in Nonlinear Control." In Encyclopedia of Systems and Control, 1–14. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5102-9_80-1.

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Conference papers on the topic "Nonlinear control methods"

1

Markley, F. Landis, John Crassidis, and Yang Cheng. "Nonlinear Attitude Filtering Methods." In AIAA Guidance, Navigation, and Control Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2005. http://dx.doi.org/10.2514/6.2005-5927.

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2

Bugajski, Daniel, Dale Enns, and Allen Tannenbaum. "Synthesis Methods for Robust Nonlinear Control." In 1993 American Control Conference. IEEE, 1993. http://dx.doi.org/10.23919/acc.1993.4792914.

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3

Fenili, André. "Nonlinear Control of a Rotating Smart Beam." In XXXVIII Iberian-Latin American Congress on Computational Methods in Engineering. Florianopolis, Brazil: ABMEC Brazilian Association of Computational Methods in Engineering, 2017. http://dx.doi.org/10.20906/cps/cilamce2017-0230.

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Harinath, Eranda, Lucas C. Foguth, Joel A. Paulson, and Richard D. Braatz. "Nonlinear model predictive control using polynomial optimization methods." In 2016 American Control Conference (ACC). IEEE, 2016. http://dx.doi.org/10.1109/acc.2016.7524882.

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Zhirabok, Alexey N., and Sergey A. Usoltsev. "Linear methods for fault diagnosis in nonlinear systems." In 2001 European Control Conference (ECC). IEEE, 2001. http://dx.doi.org/10.23919/ecc.2001.7076106.

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Taylor, James H. "Robust Nonlinear Control Based on Describing Function Methods." In ASME 1998 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/imece1998-0299.

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Abstract The robust control problem for nonlinear systems is discussed from the standpoint of the amplitude sensitivity of the nonlinear plant and final control system. Failure to recognize and accommodate this factor may give rise to nonlinear control systems that behave differently for small versus large input excitation, or perhaps exhibit limit cycles or instability. Sinusoidal-input describing functions (sidfs) are shown to be effective in dealing with amplitude sensitivity in two areas: modeling (providing plant models that achieve an excellent trade-off between conservatism and robustness) and nonlinear control synthesis. In addition, sidf-based modeling and synthesis approaches are broadly applicable. Several practical SIDF-based nonlinear compensator synthesis approaches are presented and illustrated via application to a position control problem.

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Muske, K. R., J. W. Howse, and G. A. Hansen. "Lagrangian solution methods for nonlinear model predictive control." In Proceedings of 2000 American Control Conference (ACC 2000). IEEE, 2000. http://dx.doi.org/10.1109/acc.2000.877020.

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Zwierzewicz, Zenon. "Adaptive tracking control of uncertain SISO nonlinear systems." In 2012 17th International Conference on Methods & Models in Automation & Robotics (MMAR). IEEE, 2012. http://dx.doi.org/10.1109/mmar.2012.6347863.

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Kouzoupis, D., H. J. Ferreau, H. Peyrl, and M. Diehl. "First-order methods in embedded nonlinear model predictive control." In 2015 European Control Conference (ECC). IEEE, 2015. http://dx.doi.org/10.1109/ecc.2015.7330932.

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Rigatos, Gerasimos, Pierluigi Siano, and Ivan Arsie. "Nonlinear control of valves in diesel engines using the derivative-free nonlinear Kalman Filter." In INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2014 (ICCMSE 2014). AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4897716.

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Reports on the topic "Nonlinear control methods"

1

Malisoff, Michael A., and Peter R. Wolenski. Theory, Methods, and Applications of Nonlinear Control. Fort Belvoir, VA: Defense Technical Information Center, August 2012. http://dx.doi.org/10.21236/ada582269.

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Shamma, Jeff S. Set-Valued Methods for Robust Nonlinear Control. Fort Belvoir, VA: Defense Technical Information Center, December 1998. http://dx.doi.org/10.21236/ada383800.

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Mezic, Igor. Nonlinear Dynamics and Ergodic Theory Methods in Control. Fort Belvoir, VA: Defense Technical Information Center, December 2005. http://dx.doi.org/10.21236/ada451673.

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Mezic, Igor. Nonlinear Dynamics and Ergodic Theory Methods in Control. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada418975.

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Fitzpatrick, Ben G. Idempotent Methods for Continuous Time Nonlinear Stochastic Control. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada580394.

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Rugh, Wilson J. Analysis and Design Methods for Nonlinear Control Systems. Fort Belvoir, VA: Defense Technical Information Center, March 1990. http://dx.doi.org/10.21236/ada221621.

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Banks, H. T. Computational Methods for Control of Nonlinear Fluid/Structure Problems. Fort Belvoir, VA: Defense Technical Information Center, April 1997. http://dx.doi.org/10.21236/ada329638.

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8

Ydstie, B. E. Multivariable and distributed control of nonlinear chemical processes using adaptive methods. Office of Scientific and Technical Information (OSTI), January 1988. http://dx.doi.org/10.2172/5469742.

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Tannenbaum, Allen R. Operator Theoretic Methods in the Control of Distributed and Nonlinear Systems. Fort Belvoir, VA: Defense Technical Information Center, November 1993. http://dx.doi.org/10.21236/ada274160.

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Ydstie, B. E. Multivariable and distributed control of nonlinear chemical processes using adaptive methods. Final report, February 1, 1985--January 31, 1988. Office of Scientific and Technical Information (OSTI), December 1988. http://dx.doi.org/10.2172/10136602.

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