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Journal articles on the topic 'High-order sliding mode'

Author: Grafiati

Published: 29 June 2021

Last updated: 18 February 2022

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1

Tang, W. Q., and Y. L. Cai. "High-order sliding mode control design based on adaptive terminal sliding mode." International Journal of Robust and Nonlinear Control 23, no. 2 (October 14, 2011): 149–66. http://dx.doi.org/10.1002/rnc.1820.

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2

Levant, A. "Quasi-continuous high-order sliding-mode controllers." IEEE Transactions on Automatic Control 50, no. 11 (November 2005): 1812–16. http://dx.doi.org/10.1109/tac.2005.858646.

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3

Levant, Arie, and Alon Michael. "Adjustment of high-order sliding-mode controllers." International Journal of Robust and Nonlinear Control 19, no. 15 (October 2009): 1657–72. http://dx.doi.org/10.1002/rnc.1397.

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4

Levant, Arie, and Alon Michael. "ADJUSTMENT OF HIGH-ORDER SLIDING-MODE CONTROLLERS." IFAC Proceedings Volumes 38, no. 1 (2005): 866–71. http://dx.doi.org/10.3182/20050703-6-cz-1902.00800.

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5

Utkin, Vadim, Alex Poznyak, Yury Orlov, and Andrey Polyakov. "Conventional and high order sliding mode control." Journal of the Franklin Institute 357, no. 15 (October 2020): 10244–61. http://dx.doi.org/10.1016/j.jfranklin.2020.06.018.

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6

Wang, Liang, Yongzhi Sheng, and Xiangdong Liu. "A novel adaptive high-order sliding mode control based on integral sliding mode." International Journal of Control, Automation and Systems 12, no. 3 (May 10, 2014): 459–72. http://dx.doi.org/10.1007/s12555-013-0361-9.

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7

Pan, Yaodong, Krishna Dev Kumar, and Guangjun Liu. "Reduced-order design of high-order sliding mode control system." International Journal of Robust and Nonlinear Control 21, no. 18 (December 30, 2010): 2064–78. http://dx.doi.org/10.1002/rnc.1678.

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8

Zhang, Quan Kun, Yu Yu, Shuai Mei Lian, Hong Hu, and Yu Jian Zhang. "High-Order Terminal Sliding Mode Control for Brushless Doubly-Fed Machines." Applied Mechanics and Materials 685 (October 2014): 384–88. http://dx.doi.org/10.4028/www.scientific.net/amm.685.384.

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A novel sliding-mode variable structure(SMVS) control strategy is proposed to reduce the ripples of flux and torque of brushless double-fed machines(BDFM) based on direct torque control system. In order to ensure the constant switching frequency for the inverter, two hysteresis regulators in the conventional direct torque control system system are substituted by the SMVS controllers of flux and torque，nonsingular terminal sliding modes are designed to make the motor power reach the given values in a finite period of time. and the high-order sliding mode method is adopted to estimate the chattering phenomenon of the conventional sliding mode. Meanwhile, to obtain the parameters of High-order terminal sliding mode control, a method of fuzzy neural network is presented. The simulation results show that the nonsingular high-order terminal sliding-mode control can improve the robustness and dynamic response of the system.

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9

Utkin, Vadim. "Discussion Aspects of High-Order Sliding Mode Control." IEEE Transactions on Automatic Control 61, no. 3 (March 2016): 829–33. http://dx.doi.org/10.1109/tac.2015.2450571.

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10

Oubehar, H., A. Ed-Dahhak, A. Selmani, M. Outanoute, A. Lachhab, M. Guerbaoui, M. H. Archidi, and B. Bouchikhi. "High-Order Sliding Mode Control of Greenhouse Temperature." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 3 (December 1, 2016): 548. http://dx.doi.org/10.11591/ijeecs.v4.i3.pp548-554.

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<p>This paper deals with the design and implementation of the high order sliding mode controller to control temperature greenhouse. The control objective aims to ensure a favorable microclimate for the culture development and to minimize the production cost. We propose performing regulation for the greenhouse internal temperature based on the second order sliding mode technique known as Super Twisting Algorithm (STA). This technique is able to ensure robustness with respect to bounded external disturbances. A successful feasibility study of the proposed controller is applied to maintien a desired temperature level under an experimental greenhouse. The obtained results show promising performances despite changes of the external meteorological conditions.</p>

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11

Levant, Arie. "Homogeneity approach to high-order sliding mode design." Automatica 41, no. 5 (May 2005): 823–30. http://dx.doi.org/10.1016/j.automatica.2004.11.029.

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12

Jiang, Y. A., T. Hesketh, and D. J. Clements. "High Order Sliding-mode Control of Uncertain Linear Systems." IFAC Proceedings Volumes 32, no. 2 (July 1999): 3629–34. http://dx.doi.org/10.1016/s1474-6670(17)56620-3.

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13

Benallegue, A., A. Mokhtari, and L. Fridman. "High-order sliding-mode observer for a quadrotor UAV." International Journal of Robust and Nonlinear Control 18, no. 4-5 (2008): 427–40. http://dx.doi.org/10.1002/rnc.1225.

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14

Plestan, F., A. Glumineau, and S. Laghrouche. "A new algorithm for high-order sliding mode control." International Journal of Robust and Nonlinear Control 18, no. 4-5 (2008): 441–53. http://dx.doi.org/10.1002/rnc.1234.

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15

Rhif, Ahmed, NaceurBenHadj Braiek, and Zohra Kardous. "A High-Order Sliding Mode Observer: Torpedo Guidance Application." Journal of Engineering and Technology 2, no. 1 (2012): 13. http://dx.doi.org/10.4103/0976-8580.94231.

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16

Delprat, S., and A. Ferreira de Loza. "High order sliding mode control for hybrid vehicle stability." International Journal of Systems Science 45, no. 5 (January 15, 2013): 1202–12. http://dx.doi.org/10.1080/00207721.2012.745241.

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17

Cruz-Zavala, Emmanuel, and Jaime A. Moreno. "Homogeneous High Order Sliding Mode design: A Lyapunov approach." Automatica 80 (June 2017): 232–38. http://dx.doi.org/10.1016/j.automatica.2017.02.039.

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18

Ferreira de Loza, A., L. Fridman, L. T. Aguilar, and R. Iriarte. "High‐order sliding‐mode observer–based input‐output linearization." International Journal of Robust and Nonlinear Control 29, no. 10 (April 10, 2019): 3183–99. http://dx.doi.org/10.1002/rnc.4556.

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19

Rodríguez, A., J. De León, and L. Fridman. "Quasi-continuous high-order sliding-mode controllers for reduced-order chaos synchronization." International Journal of Non-Linear Mechanics 43, no. 9 (November 2008): 948–61. http://dx.doi.org/10.1016/j.ijnonlinmec.2008.07.007.

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20

Mien, Van, Hee-Jun Kang, and Kyoo-Sik Shin. "Adaptive fuzzy quasi-continuous high-order sliding mode controller for output feedback tracking control of robot manipulators." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 228, no. 1 (May 21, 2013): 90–107. http://dx.doi.org/10.1177/0954406213490465.

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This article develops a new output feedback tracking control scheme for uncertain robot manipulators with only position measurements. Unlike the conventional sliding mode controller, a quasi-continuous second-order sliding mode controller (QC2C) is first designed. Although the QC2C produces continuous control and less chattering than conventional sliding mode and other high-order sliding mode controllers, chattering exists when the sliding manifold is defined by the equation [Formula: see text]. To alleviate the chattering, an adaptive fuzzy QC2C (FQC2C) is designed, in which the fuzzy system is used to adaptively tune the sliding mode controller gain. Furthermore, in order to eliminate chattering and achieve higher tracking accuracy, quasi-continuous third-order sliding mode controller (QC3C) and fuzzy QC3C (FQC3C) are investigated. These controllers incorporate a super-twisting second-order sliding mode observer for estimating the joint velocities, and a robust exact differentiator to estimate the sliding manifold derivative; therefore, the velocity measurement is not required. Finally, computer simulation results for a PUMA560 industrial robot are also shown to verify the effectiveness of the proposed strategy.

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21

Traoré, D., F. Plestan, A. Glumineau, and J. De Leon. "High order sliding mode control of a sensorless induction motor." IFAC Proceedings Volumes 41, no. 2 (2008): 6232–37. http://dx.doi.org/10.3182/20080706-5-kr-1001.01052.

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22

SHI, Hong-Yu, and Yong FENG. "High-order Terminal Sliding Mode Flux Observer for Induction Motors." Acta Automatica Sinica 38, no. 2 (December 19, 2012): 288–94. http://dx.doi.org/10.3724/sp.j.1004.2012.00288.

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23

Shen, Xiaoning, Jianxing Liu, Wensheng Luo, Jose Ignacio Leon, Sergio Vazquez, Abraham Marquez Alcaide, Leopoldo Garcia Franquelo, and Ligang Wu. "High-Performance Second-Order Sliding Mode Control for NPC Converters." IEEE Transactions on Industrial Informatics 16, no. 8 (August 2020): 5345–56. http://dx.doi.org/10.1109/tii.2019.2960550.

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24

Wang, Liang, Yongzhi Sheng, and Xiangdong Liu. "High-order sliding mode attitude controller design for reentry flight." Journal of Systems Engineering and Electronics 25, no. 5 (October 2014): 848–58. http://dx.doi.org/10.1109/jsee.2014.00098.

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25

Abadi, Ali Soltani Sharif, and Pooyan Alinaghi Hosseinabadi. "Adaptive terminal sliding mode control of high-order nonlinear systems." International Journal of Automation and Control 13, no. 6 (2019): 668. http://dx.doi.org/10.1504/ijaac.2019.10022590.

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26

Hosseinabadi, Pooyan Alinaghi, and Ali Soltani Sharif Abadi. "Adaptive terminal sliding mode control of high-order nonlinear systems." International Journal of Automation and Control 13, no. 6 (2019): 668. http://dx.doi.org/10.1504/ijaac.2019.102670.

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27

Beltran, B., T. Ahmed-Ali, and M. Benbouzid. "High-Order Sliding-Mode Control of Variable-Speed Wind Turbines." IEEE Transactions on Industrial Electronics 56, no. 9 (September 2009): 3314–21. http://dx.doi.org/10.1109/tie.2008.2006949.

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28

Ianagui, André S. S., and Eduardo A. Tannuri. "High Order Sliding Mode Control and Observation for DP Systems." IFAC-PapersOnLine 51, no. 29 (2018): 110–15. http://dx.doi.org/10.1016/j.ifacol.2018.09.478.

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29

Benahdouga, Seddik, Djamal Boukhetala, and Farés Boudjema. "Decentralized high order sliding mode control of multimachine power systems." International Journal of Electrical Power & Energy Systems 43, no. 1 (December 2012): 1081–86. http://dx.doi.org/10.1016/j.ijepes.2012.06.018.

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30

de Loza, Alejandra Ferreira, Francisco J. Bejarano, and Leonid Fridman. "Unmatched uncertainties compensation based on high-order sliding mode observation." International Journal of Robust and Nonlinear Control 23, no. 7 (February 20, 2012): 754–64. http://dx.doi.org/10.1002/rnc.2795.

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31

Rhif, Ahmed. "A High Order Sliding Mode Control with PID Sliding Surface: Simulation on a Torpedo." International Journal of Information Technology, Control and Automation 2, no. 1 (January 31, 2012): 107–13. http://dx.doi.org/10.5121/ijitca.2012.2101.

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32

Huangfu, Yigeng, Ruiqing Ma, and Abdellatif Miraoui. "Avoidance High-Frequency Chattering Second-Order Sliding Mode Controller Design: Buck Converter in Wind Power System." International Journal of Antennas and Propagation 2012 (2012): 1–5. http://dx.doi.org/10.1155/2012/176830.

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This paper mainly discussed a method of high-frequency second-order sliding mode control for Buck converter in wind power systems. Because the wind energy of nature is always unpredictable and intermittent, the robust control such as sliding mode control is adopted in past literatures. In order to remove the high frequency chattering problem when the traditional sliding mode achieves convergence, the second order sliding mode algorithm is reviewed firstly. Meanwhile, the Buck converter taken as a step-down converter is usually adopted in wind power system, because of its simple structure and good linearity. Under those conditions, the second order sliding mode controller is designed based on Buck converter, especially in high-power wind generation system. The experimental results illustrate that the theory of second order sliding mode can be used in high-power Buck converter. It provides one novel avoidance high frequency chattering method for the technology development of new energy generation system.

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33

Alanis, Alma Y., Gustavo Munoz-Gomez, and Jorge Rivera. "Nested High Order Sliding Mode Controller with Back-EMF Sliding Mode Observer for a Brushless Direct Current Motor." Electronics 9, no. 6 (June 24, 2020): 1041. http://dx.doi.org/10.3390/electronics9061041.

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This work presents a nested super-twisting second-order sliding mode speed controller for a brushless direct current motor with a high order sliding mode observer used for back electromotive force (back-EMF) estimation. Due to the trapezoidal nature of the back-EMF, a modified Park transformation is used in order to achieve proper field orientation. Such transformation requires information from the back-EMF that is not accessible. A second-order sliding mode observer is used to estimate the back electromotive forces needed in the modified transformation. Sliding mode control is known to be robust to matched uncertain disturbances and parametric variations but it is prone to unmatched perturbations that affect the performance of the system. A nested scheme is used to improve the response of the controller in presence of unmatched disturbances. Simulations performed under similar conditions to real-time experimentation show a good regulation of the rotor speed in terms of transient and steady-state responses along with a reduced torque ripple.

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34

Delavari, Hadi, Hamid Heydarinejad, and Dumitru Baleanu. "Adaptive fractional-order blood glucose regulator based on high-order sliding mode observer." IET Systems Biology 13, no. 2 (April 1, 2019): 43–54. http://dx.doi.org/10.1049/iet-syb.2018.5016.

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35

Wang, Xiaoyuan, Yaopeng Zhang, and Peng Gao. "Design and Analysis of Second-Order Sliding Mode Controller for Active Magnetic Bearing." Energies 13, no. 22 (November 16, 2020): 5965. http://dx.doi.org/10.3390/en13225965.

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An active magnetic bearing (AMB) is a kind of high-performance bearing that uses controllable electromagnetic force to levitate the rotor. Its control performance directly affects the operation characteristics of high-speed motors and other electromechanical products. The magnetic bearing control model is nonlinear and difficult to control. Sliding mode control algorithm can be used in the magnetic bearing control system, but the traditional sliding mode control has the problem of high-frequency chattering, which affects the operation stability of magnetic bearings. Based on the second-order sliding mode control algorithm, a new second-order sliding mode controller for active magnetic bearing control was designed, and the stability of the designed sliding mode control law was proven by Lyapunov criterion. On the basis of the established active magnetic bearing control model, the numerical analysis of the designed controller was carried out, and the control effect was compared with that obtained by the exponential reaching law for the sliding mode control algorithm. The experimental results show that the designed sliding mode controller has better dynamic performance and stability than the exponential reaching law for the sliding mode controller.

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36

Ezzat, Marwa, Alain Glumineau, and Franck Plestan. "Sensorless speed control of a permanent magnet synchronous motor: high order sliding mode controller and sliding mode observer." IFAC Proceedings Volumes 43, no. 14 (September 2010): 1290–95. http://dx.doi.org/10.3182/20100901-3-it-2016.00148.

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37

González-García, Josué, Néstor Alejandro Narcizo-Nuci, Luis Govinda García-Valdovinos, Tomás Salgado-Jiménez, Alfonso Gómez-Espinosa, Enrique Cuan-Urquizo, and Jesús Arturo Escobedo Cabello. "Model-Free High Order Sliding Mode Control with Finite-Time Tracking for Unmanned Underwater Vehicles." Applied Sciences 11, no. 4 (February 19, 2021): 1836. http://dx.doi.org/10.3390/app11041836.

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Several strategies to deal with the trajectory tracking problem of Unmanned Underwater Vehicles are encountered, from traditional controllers such as Proportional Integral Derivative (PID) or Lyapunov-based, to backstepping, sliding mode, and neural network approaches. However, most of them are model-based controllers where it is imperative to have an accurate knowledge of the vehicle hydrodynamic parameters. Despite some sliding mode and neural network-based controllers are reported as model-free, just a few of them consider a solution with finite-time convergence, which brings strong robustness and fast convergence compared with asymptotic or exponential solutions and it can also help to reduce the power consumption of the vehicle thrusters. This work aims to implement a model-free high-order sliding-mode controller and synthesize it with a time-base generator to achieve finite-time convergence. The time-base was included by parametrizing the control gain at the sliding surface. Numerical simulations validated the finite-time convergence of the controller for different time-bases even in the presence of high ocean currents. The performance of the obtained solution was also evaluated by the Root Mean Square (RMS) value of the control coefficients computed for the thrusters, as a parameter to measure the power consumption of the vehicle when following a trajectory. Computational results showed a reduction of up to 50% in the power consumption from the thrusters when compared with other solutions.

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38

Ozer, Hasan Omur, Yuksel Hacioglu, and Nurkan Yagiz. "High order sliding mode control with estimation for vehicle active suspensions." Transactions of the Institute of Measurement and Control 40, no. 5 (January 30, 2017): 1457–70. http://dx.doi.org/10.1177/0142331216685394.

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In this study, a new high order sliding mode controller (HOSMC), based on super twisting algorithm (STA), is proposed for vehicle active suspensions. It is well known that first order sliding mode controller (SMC) is insensitive to parameter variations and external disturbances. On the other hand, it suffers from chattering present in control signal that may harm the mechanical components of the system. Therefore, HOSMC is preferred in this study that attenuates chattering effectively while preserving its robustness. Proposed HOSMC uses an estimation for the equivalent part of the control signal and uses the STA for the discontinuous part of the control law. Additionally, the controller gains are obtained by offline multi-objective genetic algorithm search. Extensive simulations and experimental results are presented to reveal the performance of the proposed controller. First order SMC is also designed and used for comparison. The results indicate the superior performance of the proposed HOSMC.

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39

Fridman, Leonid, Jorge Davila, and Arie Levant. "High-Order Sliding-Mode Observation of Linear Systems with Unknown Inputs." IFAC Proceedings Volumes 41, no. 2 (2008): 4779–90. http://dx.doi.org/10.3182/20080706-5-kr-1001.00804.

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40

ZHUANG, Kai-yu. "Adaptive terminal sliding mode control for high-order nonlinear dynamic systems." Journal of Zhejiang University SCIENCE 4, no. 1 (2003): 58. http://dx.doi.org/10.1631/jzus.2003.0058.

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41

Aguilar-López, Ricardo, Rafael Martínez-Guerra, Héctor Puebla, and Rogelio Hernández-Suárez. "High order sliding-mode dynamic control for chaotic intracellular calcium oscillations." Nonlinear Analysis: Real World Applications 11, no. 1 (February 2010): 217–31. http://dx.doi.org/10.1016/j.nonrwa.2008.10.054.

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42

Fridman, Leonid, Jorge Davila, and Arie Levant. "High-order sliding-mode observation for linear systems with unknown inputs." Nonlinear Analysis: Hybrid Systems 5, no. 2 (May 2011): 189–205. http://dx.doi.org/10.1016/j.nahs.2010.09.003.

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43

Ibarra, Efrain, and Manuel Jimenez-Lizarraga. "Robust High Order Sliding Mode Optimization for Linear Time Variant Systems." IFAC Proceedings Volumes 47, no. 3 (2014): 6050–55. http://dx.doi.org/10.3182/20140824-6-za-1003.02600.

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44

Rubio-Astorga, Guillermo, Juan Diego Sanchez-Torres, Jose Canedo, and Alexander G. Loukianov. "High-Order Sliding Mode Block Control of Single-Phase Induction Motor." IEEE Transactions on Control Systems Technology 22, no. 5 (September 2014): 1828–36. http://dx.doi.org/10.1109/tcst.2013.2289307.

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45

Efimov, Denis, Leonid Fridman, Tarek Raïssi, Ali Zolghadri, and Ramatou Seydou. "Interval estimation for LPV systems applying high order sliding mode techniques." Automatica 48, no. 9 (September 2012): 2365–71. http://dx.doi.org/10.1016/j.automatica.2012.06.073.

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46

Chen, Chieh-Li, Chao Chung Peng, and Her-Terng Yau. "High-order sliding mode controller with backstepping design for aeroelastic systems." Communications in Nonlinear Science and Numerical Simulation 17, no. 4 (April 2012): 1813–23. http://dx.doi.org/10.1016/j.cnsns.2011.09.011.

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47

Mei, Keqi, Li Ma, and Shihong Ding. "Design of high‐order sliding mode controller under asymmetric output constraints." International Journal of Robust and Nonlinear Control 31, no. 15 (June 29, 2021): 7107–24. http://dx.doi.org/10.1002/rnc.5665.

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48

Jiang, Sen, Chunsheng Liu, and Yuxin Gao. "MIMO Adaptive High-Order Sliding Mode Control for Quadrotor Attitude Tracking." Journal of Aerospace Engineering 34, no. 4 (July 2021): 04021022. http://dx.doi.org/10.1061/(asce)as.1943-5525.0001271.

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49

Phuc, Bui Duc Hong, Viet-Duc Phung, Sam-Sang You, and Ton Duc Do. "Fractional-order sliding mode control synthesis of supercavitating underwater vehicles." Journal of Vibration and Control 26, no. 21-22 (February 17, 2020): 1909–19. http://dx.doi.org/10.1177/1077546320908412.

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A high-speed supercavitating vehicle is a future underwater vehicle which exploits the supercavitating propulsion technology providing a promising way to increase the vehicle speed. Robust control challenges include complex vehicle maneuvering dynamics caused by factors such as undesired switching, delayed state dependency, and nonlinearities. As effective and applicable controllers, a novel fractional-order sliding mode controller is proposed to robustly control the uncertain high-speed supercavitating vehicle system against external disturbances. The control scheme uses sliding mode control and can produce better control actions than conventional the integer-order counterpart. In this algorithm, the fractional calculus is applied to calculate the noninteger integral or derivative in the sliding mode control algorithm, providing new capabilities for uncertain high-speed supercavitating vehicle control in seeking to operate the underwater vehicle better. The performance of the proposed fractional-order sliding mode controller has been proven through analytic simulation results, which show fast responses with smooth control actions and the ability to deal with nonlinear planing force and external disturbance. One of the interesting features of the fractional-order control system is the time convergence rate of the sliding variable vector, which is greatly improved compared with the integer-order sliding mode control. Finally, the robust control system with a novel fractional-order sliding mode controller algorithm, using high flexibility of controlling undersea vehicles, can provide superior dynamical performance with stability compared with its integer-order counterpart against system uncertainties and disturbances.

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50

Goel, Ankur, and Akhilesh Swarup. "Adaptive Fuzzy High-Order Super-Twisting Sliding Mode Controller for Uncertain Robotic Manipulator." Journal of Intelligent Systems 26, no. 4 (September 26, 2017): 697–715. http://dx.doi.org/10.1515/jisys-2016-0020.

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AbstractThis paper presents a novel adaptive fuzzy high-order super-twisting sliding mode controller, based on the modified super-twisting control (STC), to achieve accurate trajectory tracking for a robotic manipulator with unknown structured uncertainties, parametric uncertainties, and time-varying external disturbances. Initially, a non-linear homogeneous sliding manifold is designed to achieve finite-time convergence, better robustness, and good transient characteristics. Afterwards, conventional STC is modified with the new sliding surface that eliminates the limitation of STC application only on relative degree 1 systems. Moreover, two adaptive fuzzy systems are designed to replace the STC signals for handling the chattering problem and overestimating the controller gains. These fuzzy systems are continuously adjusted by two adaptation laws that are deduced from the Lyapunov stability theory. These adaptive laws need only a sliding surface variable as an input and generate the optimal controller gains as an output. The finite-time convergence and stability of the proposed controller is analyzed by the homogeneous Lyapunov stability theory. Finally, to show the efficacy of the proposed method, the controller is simulated on a 2-degree-of-freedom planar robotic manipulator to obtain the accurate trajectory tracking. Simulation results demonstrate the superiority of the proposed control scheme in the presence of structured and unstructured uncertainties.

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