Thematische Bibliographien / Robust Nonlinear Adaptive Control

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Auswahl der wissenschaftlichen Literatur zum Thema „Robust Nonlinear Adaptive Control“

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Veröffentlicht am 21. Dezember 2024

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Zeitschriftenartikel zum Thema "Robust Nonlinear Adaptive Control"

Chen, Yi Mei, und Shao Ru Chen. „Nonlinear Robust Adaptive Control Using Direct Adaptive Method“. Applied Mechanics and Materials 263-266 (Dezember 2012): 817–21. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.817.

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The problem of robust adaptive stabilization of a class of multi-input nonlinear systems with unknown parameters and structure has been considered. By employing the direct adaptive method to a general nonlinear adaptive system, a robust adaptive controller is designed to complete the global asymptotically stability of the system states. Some simulations are provided to illustrate the effectiveness of the proposed method.

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Polycarpou, M. M., und P. A. Ioannou. „A robust adaptive nonlinear control design“. Automatica 32, Nr. 3 (März 1996): 423–27. http://dx.doi.org/10.1016/0005-1098(95)00147-6.

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Zerroug, N., K. Behih, Z. Bouchama und K. Zehar. „Robust Adaptive Fuzzy Control of Nonlinear Systems“. Engineering, Technology & Applied Science Research 12, Nr. 2 (09.04.2022): 8328–34. http://dx.doi.org/10.48084/etasr.4781.

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In this paper, an adaptive Fuzzy Fast Terminal Synergetic Controller (FFSC) for a certain class of SISO unknown nonlinear dynamic systems is proposed, that uses the concept of terminal attractor design, adaptive fuzzy synergetic control scheme, and Lyapunov synthesis approach. In contrast to the existing adaptive fuzzy synergetic control design, where the fuzzy systems are used to approximate the unknown system functions while the synergetic control guarantees robustness and achieves the asymptotic stability of the closed-loop system, in our technique both the continuous synergetic control law and the fuzzy approximator are derived to ensure finite-time convergence. Lyapunov conditions for finite-time stability and accuracy proofs in mathematics are presented to prove that the proposed adaptive scheme can achieve finite-time stable tracking of reference input and guarantee of the bonded system signals. Simulation results illustrate the design procedures and demonstrate the performance of the proposed controller.

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Emran, Bara. „ROBUST NONLINEAR COMPOSITE ADAPTIVE CONTROL OF QUADROTOR“. International Journal of Digital Information and Wireless Communications 4, Nr. 2 (2014): 213–25. http://dx.doi.org/10.17781/p001100.

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HASHIMOTO, Yuki, Hansheng WU und Koichi MIZUKAMI. „Adaptive Robust Control for Uncertain Nonlinear Systems“. Transactions of the Society of Instrument and Control Engineers 34, Nr. 12 (1998): 1813–21. http://dx.doi.org/10.9746/sicetr1965.34.1813.

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LI, ZHONG-HUA, TIAN-YOU CHAI, CHEONG-BOON SOH und CHANGYUN WEN. „Adaptive robust control of nonlinear uncertain systems“. International Journal of Systems Science 26, Nr. 11 (November 1995): 2159–75. http://dx.doi.org/10.1080/00207729508929160.

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Velez-Diaz, D., und Y. Tang. „Adaptive Robust Fuzzy Control of Nonlinear Systems“. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, Nr. 3 (Juni 2004): 1596–601. http://dx.doi.org/10.1109/tsmcb.2004.825934.

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Dillon, Charles H., und Jason L. Speyer. „ROBUST ADAPTIVE CONTROL OF UNCERTAIN NONLINEAR SYSTEMS“. IFAC Proceedings Volumes 35, Nr. 1 (2002): 37–42. http://dx.doi.org/10.3182/20020721-6-es-1901.00992.

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Haddad, Wassim M., Tomohisa Hayakawa und VijaySekhar Chellaboina. „Robust adaptive control for nonlinear uncertain systems“. Automatica 39, Nr. 3 (März 2003): 551–56. http://dx.doi.org/10.1016/s0005-1098(02)00244-3.

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Wang, Ding, Haibo He und Derong Liu. „Adaptive Critic Nonlinear Robust Control: A Survey“. IEEE Transactions on Cybernetics 47, Nr. 10 (Oktober 2017): 3429–51. http://dx.doi.org/10.1109/tcyb.2017.2712188.

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Dissertationen zum Thema "Robust Nonlinear Adaptive Control"

Nguyen, Canh Quang Mechanical &amp Manufacturing Engineering Faculty of Engineering UNSW. „Switching robust adaptive control in nonlinear mechanical systems“. Awarded by:University of New South Wales. School of Mechanical & Manufacturing Engineering, 2006. http://handle.unsw.edu.au/1959.4/24318.

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This work describes analysis, design, and implementation of a novel switching robust adaptive control (SRAC) method for nonlinear systems. The proposed method takes advantage of both adaptive control (AC) and robust control (RC) methods. SRAC employs one of the methods when this method is advantageous and switches to the other method when the other one becomes the preferred choice. To this end, RC is used to deal with transient effects caused by uncertainties and disturbances. The system switches over to AC for good steady state performance when certain switching criteria are satisfied. If external disturbances become dominant or new uncertainties are introduced while AC is active, the system will switch back to RC. In this manner, the switching process between AC and RC will continue to take place guaranteeing improved performance, robustness, and accuracy for the entire operation of the system. The novel idea behind the proposed method is a smart novel mechanism of bi-directional switching between RC and AC. In this mechanism, the involvement of estimators and switching rules play a decisive part in guaranteeing the smooth switching and the stability of the system. The implementation and design issues of the novel method were first evaluated by simulation on a mass spring system and then on a robot manipulator system. To control these systems with satisfactory performance, nonlinearities and uncertainties have been properly analysed and embedded into models and control algorithms. Simulation results showed the superior performance of the proposed method compared with other control methods. The experimental validation of the proposed method was conducted on a Puma 560 robot manipulator system which was established by joints 2 and 3 of the robot. Extensive comparative experimental results have validated the efficacy and superior performance of the proposed SRAC method over other control methods in the face of uncertainties and disturbances. As part of this work, a comprehensive dynamic model of robotic manipulator in the presence of joint motors, gravitational forces, friction forces and payload has been developed using MAPLE. A systematic design framework for the SRAC method has also been developed.

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Trebi-Ollennu, Ashitey. „Robust nonlinear control, designs using adaptive fuzzy systems“. Thesis, Cranfield University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296492.

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Lopez, Brett Thomas. „Adaptive robust model predictive control for nonlinear systems“. Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122395.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 115-124).
Modeling error and external disturbances can severely degrade the performance of Model Predictive Control (MPC) in real-world scenarios. Robust MPC (RMPC) addresses this limitation by optimizing over control policies but at the expense of computational complexity. An alternative strategy, known as tube MPC, uses a robust controller (designed offline) to keep the system in an invariant tube centered around a desired nominal trajectory (generated online). While tube MPC regains tractability, there are several theoretical and practical problems that must be solved for it to be used in real-world scenarios. First, the decoupled trajectory and control design is inherently suboptimal, especially for systems with changing objectives or operating conditions. Second, no existing tube MPC framework is able to capture state-dependent uncertainty due to the complexity of calculating invariant tubes, resulting in overly-conservative approximations. And third, the inability to reduce state-dependent uncertainty through online parameter adaptation/estimation leads to systematic error in the trajectory design. This thesis aims to address these limitations by developing a computationally tractable nonlinear tube MPC framework that is applicable to a broad class of nonlinear systems.
"This work was supported by the National Science Foundation Graduate Research Fellowship under Grant No. 1122374, by the DARPA Fast Lightweight Autonomy (FLA) program, by the NASA Convergent Aeronautics Solutions project Design Environment for Novel Vertical Lift Vehicles (DELIVER), and by ARL DCIST under Cooperative Agreement Number W911NF- 17-2-0181"--Page 7.
by Brett T. Lopez.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Department of Aeronautics and Astronautics

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Zhang, Zhen. „Adaptive robust periodic output regulation“. Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1187118803.

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Fisher, James Robert. „Aircraft control using nonlinear dynamic inversion in conjunction with adaptive robust control“. Texas A&M University, 2004. http://hdl.handle.net/1969.1/1515.

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This thesis describes the implementation of Yaos adaptive robust control to an aircraft control system. This control law is implemented as a means to maintain stability and tracking performance of the aircraft in the face of failures and changing aerodynamic response. The control methodology is implemented as an outer loop controller to an aircraft under nonlinear dynamic inversion control. The adaptive robust control methodology combines the robustness of sliding mode control to all types of uncertainty with the ability of adaptive control to remove steady state errors. A performance measure is developed in to reflect more subjective qualities a pilot would look for while flying an aircraft. Using this measure, comparisons of the adaptive robust control technique with the sliding mode and adaptive control methodologies are made for various failure conditions. Each control methodology is implemented on a full envelope, high fidelity simulation of the F-15 IFCS aircraft as well as on a lower fidelity full envelope F-5A simulation. Adaptive robust control is found to exhibit the best performance in terms of the introduced measure for several different failure types and amplitudes.

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Muenst, Gerhard. „Mass movement mechanism for nonlinear, robust and adaptive control of flexible structures“. Ohio : Ohio University, 2001. http://www.ohiolink.edu/etd/view.cgi?ohiou1174061987.

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Poon, Kai-yin Kenny. „An investigation on the application of nonlinear robust adaptive control theory in AC/DC power systems“. Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B38898949.

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Hayakawa, Tomohisa. „Direct Adaptive Control for Nonlinear Uncertain Dynamical Systems“. Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/5292.

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In light of the complex and highly uncertain nature of dynamical systems requiring controls, it is not surprising that reliable system models for many high performance engineering and life science applications are unavailable. In the face of such high levels of system uncertainty, robust controllers may unnecessarily sacrifice system performance whereas adaptive controllers are clearly appropriate since they can tolerate far greater system uncertainty levels to improve system performance. In this dissertation, we develop a Lyapunov-based direct adaptive and neural adaptive control framework that addresses parametric uncertainty, unstructured uncertainty, disturbance rejection, amplitude and rate saturation constraints, and digital implementation issues. Specifically, we consider the following research topics: direct adaptive control for nonlinear uncertain systems with exogenous disturbances; robust adaptive control for nonlinear uncertain systems; adaptive control for nonlinear uncertain systems with actuator amplitude and rate saturation constraints; adaptive reduced-order dynamic compensation for nonlinear uncertain systems; direct adaptive control for nonlinear matrix second-order dynamical systems with state-dependent uncertainty; adaptive control for nonnegative and compartmental dynamical systems with applications to general anesthesia; direct adaptive control of nonnegative and compartmental dynamical systems with time delay; adaptive control for nonlinear nonnegative and compartmental dynamical systems with applications to clinical pharmacology; neural network adaptive control for nonlinear nonnegative dynamical systems; passivity-based neural network adaptive output feedback control for nonlinear nonnegative dynamical systems; neural network adaptive dynamic output feedback control for nonlinear nonnegative systems using tapped delay memory units; Lyapunov-based adaptive control framework for discrete-time nonlinear systems with exogenous disturbances; direct discrete-time adaptive control with guaranteed parameter error convergence; and hybrid adaptive control for nonlinear uncertain impulsive dynamical systems.

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Patre, Parag. „Lyapunov-based robust and adaptive control of nonlinear systems using a novel feedback structure“. [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0024807.

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Poon, Kai-yin Kenny, und 潘啟然. „An investigation on the application of nonlinear robust adaptive control theory in AC/DC power systems“. Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B38898949.

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Bücher zum Thema "Robust Nonlinear Adaptive Control"

Wang, Ding, und Chaoxu Mu. Adaptive Critic Control with Robust Stabilization for Uncertain Nonlinear Systems. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-1253-3.

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International Workshop on Nonlinear and Adaptive Control: Issues in Robotics (1990 Grenoble, France). Advanced robot control: Proceedings of the International Workshop on Nonlinear and Adaptive Control, Issues in Robotics, Grenoble, France, Nov. 21-23, 1990. Berlin: Springer-Verlag, 1991.

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1961-, Sun Jing, Hrsg. Robust adaptive control. Upper Saddle River, NJ: PTR Prentice-Hall, 1996.

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Lavretsky, Eugene, und Kevin A. Wise. Robust and Adaptive Control. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4396-3.

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R, Lozano, und Ortega Romeo, Hrsg. Robust and adaptive control. Chichester: Wiley, 1988.

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Lavretsky, Eugene, und Kevin A. Wise. Robust and Adaptive Control. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-38314-4.

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Freeman, Randy A., und Petar Kokotović. Robust Nonlinear Control Design. Boston, MA: Birkhäuser Boston, 1996. http://dx.doi.org/10.1007/978-0-8176-4759-9.

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Zinober, Alan, und David Owens, Hrsg. Nonlinear and Adaptive Control. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45802-6.

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Landau, Ioan Doré, Tudor-Bogdan Airimițoaie, Abraham Castellanos-Silva und Aurelian Constantinescu. Adaptive and Robust Active Vibration Control. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-41450-8.

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1967-, Adams Richard J., Hrsg. Robust multivariable flight control. London: Springer-Verlag, 1994.

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Buchteile zum Thema "Robust Nonlinear Adaptive Control"

Siqueira, Adriano A. G., Marco H. Terra und Marcel Bergerman. „Adaptive Nonlinear $${{\mathcal{H}}}_{\user2{\infty}}$$ Control“. In Robust Control of Robots, 59–73. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-898-0_4.

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Kwatny, Harry G., und Gilmer L. Blankenship. „Robust and Adaptive Control Systems“. In Nonlinear Control and Analytical Mechanics, 227–66. Boston, MA: Birkhäuser Boston, 2000. http://dx.doi.org/10.1007/978-1-4612-2136-4_7.

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Fradkov, Alexander L., Iliya V. Miroshnik und Vladimir O. Nikiforov. „Adaptive and Robust Control Design“. In Nonlinear and Adaptive Control of Complex Systems, 265–389. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-015-9261-1_6.

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DeHaan, Darryl, Martin Guay und Veronica Adetola. „Adaptive Robust MPC: A Minimally-Conservative Approach“. In Nonlinear Model Predictive Control, 55–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01094-1_4.

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Khorrami, Farshad, Prashanth Krishnamurthy und Hemant Melkote. „Robust Adaptive Control of Stepper Motors“. In Modeling and Adaptive Nonlinear Control of Electric Motors, 109–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-08788-6_7.

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Wen, Guo-Xing, Yan-Jun Liu und C. L. Philip Chen. „Adaptive Robust NN Control of Nonlinear Systems“. In Advances in Neural Networks – ISNN 2011, 535–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21090-7_62.

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Tomei, P. „An Adaptive PD Control Algorithm for Robots“. In Robust Control of Linear Systems and Nonlinear Control, 575–82. Boston, MA: Birkhäuser Boston, 1990. http://dx.doi.org/10.1007/978-1-4612-4484-4_57.

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Krishnamurthy, P., F. Khorrami und Ƶ. Wang. „Robust Adaptive Nonlinear Control for Robotic Manipulators with Flexible Joints“. In Adaptive Control for Robotic Manipulators, 317–36. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2016. http://dx.doi.org/10.1201/9781315166056-14.

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Ortega, Romeo, Yu Tang und Laurent Praly. „On Bounded Adaptive Control with Reduced Prior Knowledge“. In Robust Control of Linear Systems and Nonlinear Control, 361–68. Boston, MA: Birkhäuser Boston, 1990. http://dx.doi.org/10.1007/978-1-4612-4484-4_35.

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Campion, G., und G. Bastin. „On Adaptive Linearizing Control of Omnidirectional Mobile Robots“. In Robust Control of Linear Systems and Nonlinear Control, 531–38. Boston, MA: Birkhäuser Boston, 1990. http://dx.doi.org/10.1007/978-1-4612-4484-4_53.

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Konferenzberichte zum Thema "Robust Nonlinear Adaptive Control"

Yao, Bin, George T. C. Chiu und John T. Reedy. „Nonlinear Adaptive Robust Control of One-DOF Electro-Hydraulic Servo Systems“. In ASME 1997 International Mechanical Engineering Congress and Exposition, 191–97. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/imece1997-1301.

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Abstract This paper intends to provide a theoretic framework for the high performance robust control of electro-hydraulic servo-systems. This is achieved through applying the recently proposed adaptive robust control (ARC) while taking into account the particular nonlinearities and model uncertainties of the electro-hydraulic servo-systems. In particular, the robust motion control of a typical electro-hydraulic servo-system will be considered. The system consists of an inertia load driven by a double rod cylinder regulated by a two-stage servovalve. The paper will consider the effect of both parametric uncertainties coming from the inertia load and the cylinder and the uncertain nonlinearities such as friction forces. Non-differentiability of the inherent nonlinearities associated with hydraulic dynamics is examined and strategies are provided for handling the non-differentiability of the control term due to the direct change of valve opening when doing backstepping design via ARC Lyapunov function. The resulting controller guarantees a prescribed transient performance and final tracking accuracy in the presence of both parametric uncertainties and uncertain nonlinearities while achieving asymptotic tracking in the presence of parameteric uncertainties.

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Long, Yu, Hang Zhong, Zhenyu Qian, Min Liu, Hui Zhang, Yiming Jiang und Yaonan Wang. „Robust Nonlinear Observer-based Adaptive Visual Servo Control for a Multirotor“. In 2024 IEEE International Conference on Real-time Computing and Robotics (RCAR), 124–29. IEEE, 2024. http://dx.doi.org/10.1109/rcar61438.2024.10671231.

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Polycarpou, Marios M., und Petros A. Ioannou. „A Robust Adaptive Nonlinear Control Design“. In 1993 American Control Conference. IEEE, 1993. http://dx.doi.org/10.23919/acc.1993.4793094.

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Gopalswamy, Swaminathan, und J. Karl Hedrick. „Robust Adaptive Control of Multivariable Nonlinear Systems“. In 1990 American Control Conference. IEEE, 1990. http://dx.doi.org/10.23919/acc.1990.4791131.

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Wahab, H. F., und R. Katebi. „Robust adaptive estimators for nonlinear systems“. In 2013 Conference on Control and Fault-Tolerant Systems (SysTol). IEEE, 2013. http://dx.doi.org/10.1109/systol.2013.6693823.

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Adetona, O., S. Sathananthan und L. H. Keel. „Robust nonlinear adaptive control using neural networks“. In Proceedings of American Control Conference. IEEE, 2001. http://dx.doi.org/10.1109/acc.2001.946247.

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Li, Dawei, Ming Xin und Bin Jia. „Robust Nonlinear Filter Using Adaptive Edgeworth Expansion“. In 2018 Annual American Control Conference (ACC). IEEE, 2018. http://dx.doi.org/10.23919/acc.2018.8430825.

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Gopalswamy, Swaminathan, und J. Karl Hedrick. „Robust Adaptive Nonlinear Control of High Peformance Aircraft“. In 1991 American Control Conference. IEEE, 1991. http://dx.doi.org/10.23919/acc.1991.4791589.

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Yu, Wenwu, und Guanrong Chen. „Robust adaptive flocking control of nonlinear multi-agent systems“. In Control (MSC). IEEE, 2010. http://dx.doi.org/10.1109/cacsd.2010.5612787.

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Yao, Bin. „Desired Compensation Adaptive Robust Control“. In ASME 1998 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/imece1998-0303.

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Abstract A desired compensation adaptive robust control (DCARC) approach is proposed for nonlinear systems having both parametric uncertainties and uncertain nonlinearities. DCARC of nonlinear systems transformable to a normal form is first solved. A DCARC backstepping design is then developed to overcome the design difficulties associated with unmatched model uncertainties. The proposed DCARC has the unique feature that the adaptive model compensation part depends on the reference trajectory and parameter estimates only. Such a structure has several implementation advantages. First, the regressor in the model compensation part can be calculated off-line and on-line computation time may be reduced. Second, the interaction between the parameter adaptation and the robust control law is minimized, which may facilitate the controller gain tuning process considerably. Third, the effect of measurement noise is minimized since the regressor does not depend on actual measurements. As a result, a fast adaptation rate may be chosen in implementation to speed up the transient response and to improve overall tracking performance. These claims have been verified in the comparative experimental studies for the control of robot manipulators.

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Berichte der Organisationen zum Thema "Robust Nonlinear Adaptive Control"

Hovakimyan, Naira. Robust Adaptive Control of Multivariable Nonlinear Systems. Fort Belvoir, VA: Defense Technical Information Center, November 2008. http://dx.doi.org/10.21236/ada501711.

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Hovakimyan, Naira. Robust Adaptive Control of Multivariable Nonlinear Systems. Fort Belvoir, VA: Defense Technical Information Center, März 2011. http://dx.doi.org/10.21236/ada565190.

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Tatlicioglu, E., M. McIntyre, D. Dawson und I. Walker. Adaptive Nonlinear Tracking Control of Kinematically Redundant Robot Manipulators with Sub-Task Extensions. Fort Belvoir, VA: Defense Technical Information Center, Januar 2005. http://dx.doi.org/10.21236/ada465620.

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Valavani, Lena. Robust and Adaptive Control. Fort Belvoir, VA: Defense Technical Information Center, April 1990. http://dx.doi.org/10.21236/ada224810.

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Ikeda, Yutaka, James Ramsey, Eugene Lavretsky und Patrick McCormick. Robust Adaptive Control of UCAVs. Fort Belvoir, VA: Defense Technical Information Center, September 2004. http://dx.doi.org/10.21236/ada427938.

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Teel, Andrew R. Optimization-Based Robust Nonlinear Control. Fort Belvoir, VA: Defense Technical Information Center, August 2006. http://dx.doi.org/10.21236/ada452020.

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McEneaney, William M. Nonlinear Robust Control and Estimation. Fort Belvoir, VA: Defense Technical Information Center, November 1999. http://dx.doi.org/10.21236/ada383810.

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Kezunovic, Mladen, Shmuel Oren, Kory Hedman, Erick Moreno Centeno, Garng Huang und Alex Sprintson. Robust Adaptive Topology Control Project (RATC). Office of Scientific and Technical Information (OSTI), Juli 2015. http://dx.doi.org/10.2172/1209678.

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Sastry, Shankar S. Adaptive and Nonlinear Control Methodologies. Fort Belvoir, VA: Defense Technical Information Center, August 1988. http://dx.doi.org/10.21236/ada200694.

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Doyle, John C. Nonlinear Robust Control Theory and Applications. Fort Belvoir, VA: Defense Technical Information Center, November 1998. http://dx.doi.org/10.21236/ada367040.

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