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Journal articles on the topic 'Robust Nonlinear Adaptive Control'

Author: Grafiati

Published: 21 December 2024

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1

Chen, Yi Mei, and Shao Ru Chen. "Nonlinear Robust Adaptive Control Using Direct Adaptive Method." Applied Mechanics and Materials 263-266 (December 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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2

Polycarpou, M. M., and P. A. Ioannou. "A robust adaptive nonlinear control design." Automatica 32, no. 3 (March 1996): 423–27. http://dx.doi.org/10.1016/0005-1098(95)00147-6.

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3

Zerroug, N., K. Behih, Z. Bouchama, and K. Zehar. "Robust Adaptive Fuzzy Control of Nonlinear Systems." Engineering, Technology & Applied Science Research 12, no. 2 (April 9, 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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4

Emran, Bara. "ROBUST NONLINEAR COMPOSITE ADAPTIVE CONTROL OF QUADROTOR." International Journal of Digital Information and Wireless Communications 4, no. 2 (2014): 213–25. http://dx.doi.org/10.17781/p001100.

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5

HASHIMOTO, Yuki, Hansheng WU, and Koichi MIZUKAMI. "Adaptive Robust Control for Uncertain Nonlinear Systems." Transactions of the Society of Instrument and Control Engineers 34, no. 12 (1998): 1813–21. http://dx.doi.org/10.9746/sicetr1965.34.1813.

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6

LI, ZHONG-HUA, TIAN-YOU CHAI, CHEONG-BOON SOH, and CHANGYUN WEN. "Adaptive robust control of nonlinear uncertain systems." International Journal of Systems Science 26, no. 11 (November 1995): 2159–75. http://dx.doi.org/10.1080/00207729508929160.

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7

Velez-Diaz, D., and Y. Tang. "Adaptive Robust Fuzzy Control of Nonlinear Systems." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 34, no. 3 (June 2004): 1596–601. http://dx.doi.org/10.1109/tsmcb.2004.825934.

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8

Dillon, Charles H., and Jason L. Speyer. "ROBUST ADAPTIVE CONTROL OF UNCERTAIN NONLINEAR SYSTEMS." IFAC Proceedings Volumes 35, no. 1 (2002): 37–42. http://dx.doi.org/10.3182/20020721-6-es-1901.00992.

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9

Haddad, Wassim M., Tomohisa Hayakawa, and VijaySekhar Chellaboina. "Robust adaptive control for nonlinear uncertain systems." Automatica 39, no. 3 (March 2003): 551–56. http://dx.doi.org/10.1016/s0005-1098(02)00244-3.

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10

Wang, Ding, Haibo He, and Derong Liu. "Adaptive Critic Nonlinear Robust Control: A Survey." IEEE Transactions on Cybernetics 47, no. 10 (October 2017): 3429–51. http://dx.doi.org/10.1109/tcyb.2017.2712188.

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11

Landau, Ioan D. "Evolution of Adaptive Control." Journal of Dynamic Systems, Measurement, and Control 115, no. 2B (June 1, 1993): 381–91. http://dx.doi.org/10.1115/1.2899078.

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The evolution of the adaptive control algorithms driven by the results obtained in the application of the 1st generation of adaptive controllers (model reference adaptive control, self-tuning minimum variance) is examined. Research in the field of adaptive control has been directed, on the one hand, toward the development of a robust general purpose adaptive controller and, on the other, towards the extension of the model reference adaptive control approach to nonlinear systems. Research has also investigated the stability/passivity approach for developing dedicated adaptive control algorithms for particular classes of nonlinear plants (e.g., rigid robots). The paper will review the results obtained in these directions both from the theoretical and the practical points of view. In the final part, current research directions will be included.

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12

Zhang, Jiaxu, Shiying Zhou, Fengjun Li, and Jian Zhao. "Integrated nonlinear robust adaptive control for active front steering and direct yaw moment control systems with uncertainty observer." Transactions of the Institute of Measurement and Control 42, no. 16 (August 31, 2020): 3267–80. http://dx.doi.org/10.1177/0142331220949718.

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This paper presents an integrated nonlinear robust adaptive controller with uncertainty observer for active front wheel steering system and direct yaw moment control system. First, an integrated vehicle chassis control model is established as the nominal model with the additive and multiplicative uncertainties of the system. Secondly, an integrated nonlinear robust adaptive control law with the additive uncertainty observer is designed via Lyapunov stability theory to calculate the corrective yaw moment, and an adaptive law is designed based on projection correction method to online estimate and compensate the multiplicative uncertainty of the system. Then, the constrained optimal allocation problem of the corrective yaw moment is transformed into the nonlinear optimization problem, and the sequential quadratic programming method is used to solve the nonlinear optimization problem to coordinate active front wheel steering system and direct yaw moment control system. Finally, the performance of the proposed integrated nonlinear robust adaptive controller is verified via vehicle dynamics simulation software.

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13

Efimov, D. V., and I. Yu Tyukin. "DIRECT ROBUST ADAPTIVE NONLINEAR CONTROL with DERIVATIVES ESTIMATION." IFAC Proceedings Volumes 38, no. 1 (2005): 229–34. http://dx.doi.org/10.3182/20050703-6-cz-1902.00259.

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14

Li, Chanying, Liang-Liang Xie, and Lei Guo. "ROBUST STABILITY OF DISCRETE-TIME ADAPTIVE NONLINEAR CONTROL." IFAC Proceedings Volumes 38, no. 1 (2005): 298–303. http://dx.doi.org/10.3182/20050703-6-cz-1902.00994.

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15

Ikhouane, Fayçal, Abderrahman Rabeh, and Fouad Giri. "Transient performance analysis in robust nonlinear adaptive control." Systems & Control Letters 31, no. 1 (June 1997): 21–31. http://dx.doi.org/10.1016/s0167-6911(97)00020-0.

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16

Sun, Liang, and Wei Huo. "Nonlinear Robust Adaptive Control for Spacecraft Proximity Operations." IFAC Proceedings Volumes 47, no. 3 (2014): 2219–24. http://dx.doi.org/10.3182/20140824-6-za-1003.00747.

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17

Liu, Ning, Yu Sheng Liu, and Qiang Yang. "Robust Adaptive Control of Nonlinear Systems with Uncertainties." Applied Mechanics and Materials 568-570 (June 2014): 1108–12. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.1108.

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This paper proposes a robust adaptive robust controller for nonlinear systems represented by input-output models with unmodeled dynamics. Under the circumstances that the output of the system is bounded, the proposed controller can guarantee that all the variables of the system are bounded in the presence of unmodeled dynamics and time-varying disturbances. The scheme does not need to generate an additional dynamic signal to dominate the effects of the unmodeled dynamics. It is shown that the mean-square tracking error can be made arbitrarily small by choosing some design parameters appropriately.

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18

Mizumoto, Ikuro, Ratna Bhushan Gopaluni, Sirish L. Shah, and Zenta Iwai. "ROBUST ADAPTIVE CONTROL FOR STRICT-FEEDBACK NONLINEAR SYSTEMS." Asian Journal of Control 7, no. 3 (October 22, 2008): 231–43. http://dx.doi.org/10.1111/j.1934-6093.2005.tb00233.x.

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19

Galvan-Perez, Daniel, Hugo Yañez-Badillo, Francisco Beltran-Carbajal, Ivan Rivas-Cambero, Antonio Favela-Contreras, and Ruben Tapia-Olvera. "Neural Adaptive Robust Motion-Tracking Control for Robotic Manipulator Systems." Actuators 11, no. 9 (September 7, 2022): 255. http://dx.doi.org/10.3390/act11090255.

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This paper deals with the motion trajectory tracking control problem based on output feedback and artificial neural networks for anthropomorphic manipulator robots under disturbed operating scenarios. This class of manipulator robots constitutes nonlinear dynamic systems subjected to disturbance torques induced mainly by work payload. Parametric uncertainty and possible dynamic modeling errors stand for other kind of disturbances that can deteriorate the efficiency and robustness of the tracking of controlled nonlinear robotic system trajectories. In fact, the presence of unknown dynamic disturbances is unavoidable in industrial robotic engineering systems. Therefore, for high-precision applications, such as laser cutting, marking, or welding, effective control schemes should be designed to guarantee adequate motion profile tracking planned on this class of disturbed nonlinear robotic system. In this context, a new adaptive robust motion trajectory tracking control scheme based on output feedback and artificial neural networks of anthropomorphic manipulator robots is presented. Three-layer B-spline artificial neural networks and time-series modeling are properly exploited in the design of novel adaptive robust motion tracking controllers for robotic applications of laser manufacturing. In this way, dependency on detailed nonlinear mathematical modeling of robotic systems is considerably reduced, and real-time estimation of uncertain dynamic disturbances is not required. Furthermore, several cases studies to demonstrate the motion planning tracking control robustness for a class of MIMO nonlinear robotic systems are described. blue Insights for the extension of the introduced output-feedback adaptive neural control design approach for other architecture of nonlinear robotic systems are depicted.

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20

Asadi, Davood, and Karim Ahmadi. "Nonlinear robust adaptive control of an airplane with structural damage." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 234, no. 14 (May 20, 2020): 2076–88. http://dx.doi.org/10.1177/0954410020926618.

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This article investigates the design of a novel nonlinear robust adaptive control architecture to stabilize and control an airplane in the presence of left-wing damage. Damage effect is modeled by considering the sudden mass and inertia changes, center of gravity, and aerodynamic variations. The novel nonlinear control algorithm applies a state predictor as well as the error between the real damaged dynamics and a virtual model based on the nominal aircraft dynamics in the control loop of the adaptive strategy. The projection operator is used for the purpose of robustness of the adaptive control algorithm. The stability of the proposed nonlinear robust adaptive controller is demonstrated applying the Lyapunov stability theory. The performance of the proposed controller is compared with two previous successful algorithms, which are implemented on the Generic Transport Model airplane to accommodate wing damage. Numerical simulations demonstrate the effectiveness and advantages of the proposed robust adaptive algorithm regarding two other algorithms of adaptive sliding mode and L 1 adaptive control.

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21

Chen, Jiao, Jiangyun Wang, and Weihong Wang. "Robust Adaptive Control for Nonlinear Aircraft System with Uncertainties." Applied Sciences 10, no. 12 (June 22, 2020): 4270. http://dx.doi.org/10.3390/app10124270.

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Model reference adaptive control (MRAC) schemes are known as an effective method to deal with system uncertainties. High adaptive gains are usually needed in order to achieve fast adaptation. However, this leads to high-frequency oscillation in the control signal and may even make the system unstable. A robust adaptive control architecture was designed in this paper for nonlinear aircraft dynamics facing the challenges of input uncertainty, matched uncertainty, and unmatched uncertainty. By introducing a robust compensator to the MRAC framework, the high-frequency components in the control response were eliminated. The proposed control method was applied to the longitudinal-direction motion control of a nonlinear aircraft system. Flight simulation results demonstrated that the proposed robust adaptive method was able to achieve fast adaptation without high-frequency oscillations, and guaranteed transient performance.

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22

Zhang, Tao, Yuanxun Fan, and Haodong Qiu. "Research on electro-hydraulic position servo synchronous control system based on adaptive robust control." Journal of Physics: Conference Series 2760, no. 1 (May 1, 2024): 012033. http://dx.doi.org/10.1088/1742-6596/2760/1/012033.

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Abstract A control method combining adaptive robust control with cross-coupling control is designed to address the issues of model uncertainty, nonlinear friction, and external interference in electro-hydraulic servo systems. We have established a nonlinear model of the system and designed an adaptive robust controller. Further simulation studies have shown that the control method designed in this paper has better control performance and stronger anti-interference ability than PID control and still has good control performance under continuous interference.

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23

Zhang, Chao, Xing Wang, Zhengfeng Ming, and Zhuang Cai. "Enhanced Nonlinear Robust Control for TCSC in Power System." Mathematical Problems in Engineering 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/1416059.

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This paper proposes an enhanced robust control method, which is for thyristor controlled series compensator (TCSC) in presences of time-delay nonlinearity, uncertain parameter, and external disturbances. Unlike conventional adaptive control methods, the uncertain parameter is estimated by using system immersion and manifold invariant (I&I) adaptive control. Thus, the oscillation of states caused by the coupling between parameter estimator and system states can be avoided. In addition, in order to overcome the influences of time-delay nonlinearity and external disturbances, backstepping sliding mode control is adopted to design control law recursively. Furthermore, robustness of TCSC control subsystem is achievable provided that dissipation inequality is satisfied in each step. Effectiveness and efficiencies of the proposed control method are verified by simulations. Compared with adaptive backstepping sliding mode control and adaptive backstepping control, the time of reaching steady state is shortened by at least 11% and the oscillation amplitudes of transient responses are reduced by at most 50%.

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24

Wu, Guanghui, Xiuyun Meng, and Jie Wang. "Robust Adaptive Nonlinear Dynamic Inversion Control for an Air-breathing Hypersonic Vehicle." MATEC Web of Conferences 220 (2018): 08001. http://dx.doi.org/10.1051/matecconf/201822008001.

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This paper presents a robust adaptive nonlinear dynamic inversion control approach for the longitudinal dynamics of an air-breathing hypersonic vehicle. The proposed approach adopts a fast adaptation law using high-gain learning rate, while a low-pass filter is synthesized with the modified adaptive scheme to filter out the high-frequency content of the estimates. This modified high-gain adaptive scheme achieves a good transient process and a nice robust property with respect to parameter uncertainties, without exciting high-frequency oscillations. Based on input-output linearization, the nonlinear hypersonic dynamics are transformed into equivalent linear systems. Therefore, the pole placement technique is applied to design the baseline nonlinear dynamic inversion controller. Finally, the simulation results of the modified adaptive nonlinear dynamic inversion control law demonstrate the proposed control approach provides robust tracking of reference trajectories.

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25

Feng, Lijun, and Hao Yan. "Nonlinear Adaptive Robust Control of the Electro-Hydraulic Servo System." Applied Sciences 10, no. 13 (June 29, 2020): 4494. http://dx.doi.org/10.3390/app10134494.

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This paper focuses on high performance adaptive robust position control of electro-hydraulic servo system. The main feature of the paper is the combination of adaptive robust algorithm with discrete disturbance estimation to cope with the parametric uncertainties, uncertain nonlinearities, and external disturbance in the hydraulic servo system. First of all, a mathematical model of the single-rod position control system is developed and a nonlinear adaptive robust controller is proposed using the backstepping design technique. Adaptive robust control is used to encompass the parametric uncertainties and uncertain nonlinearities. Subsequently, a discrete disturbance estimator is employed to compensate for the effect of strong external disturbance. Furthermore, a special Lyapunov function is formulated to handle unknown nonlinear parameters in the system state equations. Simulations are carried out, and the results validate the superior performance and robustness of the proposed method.

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26

Li, Hong Lin, and Peng Bing Zhao. "Research of Robust Adaptive Fuzzy Control in Manipulator." Advanced Materials Research 317-319 (August 2011): 713–17. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.713.

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There are friction characteristics, random disturbance, load variation and other nonlinear influencing factors in the multi-joint manipulator system generally. According to the problem that the traditional PID and fuzzy control are difficult to achieve rapid and high-precision control for this kind of system, a kind of robust adaptive fuzzy controller was designed based on fuzzy compensation under the circumstances that the fuzzy information can be known and all the state variables can be measured. Simultaneously, in order to reduce the computational load of fuzzy approximation and improve the efficiency of mathematical operation, a method that distinguishing different disturbance compensatory terms and approximating each of them respectively was adopted. The simulation results show that the robust adaptive fuzzy controller based on fuzzy compensation can restrain friction, disturbance, load variation and other nonlinear influencing factors.

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27

Green, Scott A., and Kevin C. Craig. "Robust, Digital, Nonlinear Control of Magnetic-Levitation Systems." Journal of Dynamic Systems, Measurement, and Control 120, no. 4 (December 1, 1998): 488–95. http://dx.doi.org/10.1115/1.2801490.

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This paper presents a robust, adaptive, nonlinear controller for a class of magnetic-levitation systems, which includes active-magnetic bearings. The controller is analytically and experimentally shown to be superior to a classical linear control system in stability, control effort, step-response performance, robustness to parameter variations, and force-disturbance rejection performance. Using an adaptive backstepping approach, a Lyapunov function is generated along with an adaptive control law such that the nonlinear, closed-loop, continuous system is shown to guarantee stability of the equilibrium and convergence of the parameter estimates to constant values. The control system error coordinates are proven to be bounded in the presence of a bounded force disturbance input. The novelty of this controller is that it is digitally implemented using Euler integrators with anti-windup limits, it is single-input-single-output requiring only a measurement of the position of the levitating object, and it is designed to adaptively estimate not only the uncertain model parameters, but also the constant forces applied to the levitating object in order to ensure robustness to force disturbances. The experimental study was conducted on a single-axis magnetic-levitation device. The controller is shown to be applicable to active-magnetic bearings, under specific conditions, as well as any magnetic-levitation system that can be represented in output-feedback form.

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28

Wang, Sanxiu, Kexin Xing, and Zhengchu Wang. "Adaptive Fuzzy H∞ Robust Tracking Control for Nonlinear MIMO Systems." Cybernetics and Information Technologies 15, no. 1 (March 1, 2015): 34–45. http://dx.doi.org/10.1515/cait-2015-0004.

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Abstract In this paper an adaptive fuzzy H∞ robust tracking control scheme is developed for a class of uncertain nonlinear Multi-Input and Multi-Output (MIMO) systems. Firstly, fuzzy logic systems are introduced to approximate the unknown nonlinear function of the system by an adaptive algorithm. Next, a H∞ robust compensator controller is employed to eliminate the effect of the approximation error and external disturbances. Consequently, a fuzzy adaptive robust controller is proposed, such that the tracking error of the resulting closed-loop system converges to zero and the tracking robustness performance can be guaranteed. The simulation results performed on a two-link robotic manipulator demonstrate the validity of the proposed control scheme.

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29

Bui, Trong Hieu, and Quoc Toan Truong. "Control of active suspension system using H∞ and adaptive robust controls." Science and Technology Development Journal 18, no. 1 (March 31, 2015): 5–13. http://dx.doi.org/10.32508/stdj.v18i1.917.

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This paper presents a control of active suspension system for quarter-car model with two-degree-of-freedom using H∞ and nonlinear adaptive robust control method. Suspension dynamics is linear and treated by H∞ method which guarantees the robustness of closed loop system under the presence of uncertainties and minimizes the effect of road disturbance to system. An Adaptive Robust Control (ARC) technique is used to design a force controller such that it is robust against actuator uncertainties. Simulation results are given for both frequency and time domains to verify the effectiveness of the designed controllers.

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30

Li, Shujiang, and Wei Wang. "Adaptive Backstepping Robust Control of Nonlinear Spray Boom System." Journal of Advanced Agricultural Technologies 6, no. 4 (2019): 246–52. http://dx.doi.org/10.18178/joaat.6.4.246-252.

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31

LIAO, Teh-Lu. "Adaptive Robust Control for Nonlinear Systems with Mismatched Uncertainties." JSME International Journal Series C 41, no. 4 (1998): 751–58. http://dx.doi.org/10.1299/jsmec.41.751.

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32

GE, S. S. "Robust adaptive NN feedback linearization control of nonlinear systems." International Journal of Systems Science 27, no. 12 (December 1996): 1327–38. http://dx.doi.org/10.1080/00207729608929339.

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33

Ma, Zongcheng, Jinfu Feng, Junhua Hu, and An Liu. "Nonlinear robust adaptive NN control for variable-sweep aircraft." Journal of Vibroengineering 20, no. 1 (February 15, 2018): 368–84. http://dx.doi.org/10.21595/jve.2017.18619.

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34

Xu, Haojian, and Petros A. Ioannou. "ROBUST ADAPTIVE CONTROL OF LINEARIZABLE NONLINEAR SINGLE INPUT SYSTEMS." IFAC Proceedings Volumes 35, no. 1 (2002): 391–96. http://dx.doi.org/10.3182/20020721-6-es-1901.01051.

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35

Gao, Fangzheng, Fushun Yuan, and Hejun Yao. "Robust adaptive control for nonholonomic systems with nonlinear parameterization." Nonlinear Analysis: Real World Applications 11, no. 4 (August 2010): 3242–50. http://dx.doi.org/10.1016/j.nonrwa.2009.11.019.

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36

Ding, Zhengtao. "Robust Adaptive Control of Nonlinear Systems with Bounded Disturbances." IFAC Proceedings Volumes 29, no. 1 (June 1996): 5096–101. http://dx.doi.org/10.1016/s1474-6670(17)58489-x.

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37

Zhang, Youping, and Petros A. Ioannou. "Linear Robust Adaptive Control Design Using a Nonlinear Approach." IFAC Proceedings Volumes 29, no. 1 (June 1996): 5162–67. http://dx.doi.org/10.1016/s1474-6670(17)58500-6.

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38

Feng, Weining, and I. Postlethwaite. "Robust Nonlinear H ∞ /Adaptive Control of Robot Manipulator Motion." IFAC Proceedings Volumes 26, no. 2 (July 1993): 31–34. http://dx.doi.org/10.1016/s1474-6670(17)48888-4.

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39

Yang, Zi-Jiang, and Michitaka Tateishi. "Adaptive robust nonlinear control of a magnetic levitation system." Automatica 37, no. 7 (July 2001): 1125–31. http://dx.doi.org/10.1016/s0005-1098(01)00063-2.

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40

Li, Chanying, and Liang-Liang Xie. "On robust stability of discrete-time adaptive nonlinear control." Systems & Control Letters 55, no. 6 (June 2006): 452–58. http://dx.doi.org/10.1016/j.sysconle.2005.09.008.

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41

YAO, Jianyong, Zongxia JIAO, Bin YAO, Yaoxing SHANG, and Wenbin DONG. "Nonlinear Adaptive Robust Force Control of Hydraulic Load Simulator." Chinese Journal of Aeronautics 25, no. 5 (October 2012): 766–75. http://dx.doi.org/10.1016/s1000-9361(11)60443-3.

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42

Yang, Zi-Jiang, and Michitaka Tateishi. "Robust adaptive nonlinear control of a magnetic levitation system." IFAC Proceedings Volumes 32, no. 2 (July 1999): 4422–27. http://dx.doi.org/10.1016/s1474-6670(17)56754-3.

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43

Kanellakopoulos, I., P. V. Kokotovic, and R. Marino. "An extended direct scheme for robust adaptive nonlinear control." Automatica 27, no. 2 (March 1991): 247–55. http://dx.doi.org/10.1016/0005-1098(91)90075-d.

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44

Kosmatopoulos, E. B., and P. A. Ioannou. "Robust switching adaptive control of multi-input nonlinear systems." IEEE Transactions on Automatic Control 47, no. 4 (April 2002): 610–24. http://dx.doi.org/10.1109/9.995038.

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45

Huaguang, Zhang, and Zhang Mingjun. "Robust direct adaptive fuzzy control for nonlinear MIMO systems." Progress in Natural Science 16, no. 10 (October 1, 2006): 1098–105. http://dx.doi.org/10.1080/10020070612330116.

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46

Liu, Y., and X. Y. Li. "Robust adaptive control of nonlinear systems with unmodelled dynamics." IEE Proceedings - Control Theory and Applications 151, no. 1 (January 1, 2004): 83–88. http://dx.doi.org/10.1049/ip-cta:20040063.

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47

WANG, Qiang-De, and Chun-Ling WEI. "Robust Adaptive Control of Nonholonomic Systems with Nonlinear Parameterization." Acta Automatica Sinica 33, no. 4 (April 2007): 399–403. http://dx.doi.org/10.1360/aas-007-0399.

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48

Rysdyk, R., and A. J. Calise. "Robust nonlinear adaptive flight control for consistent handling qualities." IEEE Transactions on Control Systems Technology 13, no. 6 (November 2005): 896–910. http://dx.doi.org/10.1109/tcst.2005.854345.

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49

Hou, Ming-Zhe, and Guang-Ren Duan. "Robust adaptive dynamic surface control of uncertain nonlinear systems." International Journal of Control, Automation and Systems 9, no. 1 (February 2011): 161–68. http://dx.doi.org/10.1007/s12555-011-0121-7.

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Liu, Xiangbin, Hongye Su, Bin Yao, and Jian Chu. "Adaptive robust control of nonlinear systems with dynamic uncertainties." International Journal of Adaptive Control and Signal Processing 23, no. 4 (April 2009): 353–77. http://dx.doi.org/10.1002/acs.1048.

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