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Reliability analysis of a multi-eso based control strategy for level adjustment control system of quadruped robot under disturbances and failures

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Warianty tytułu
PL
Analiza niezawodności strategii sterowania w oparciu o kilka obserwatorów stanu rozszerzonego ESO i jej zastosowanie w systemie kontroli regulacji poziomu czworonożnego robota w warunkach zakłóceń i uszkodzeń
Języki publikacji
EN
Abstrakty
EN
The complexity of control algorithms and their vulnerability to disturbances and failures are the main problems that restrict the operations of multi-legged mobile robots in more complex environments. In this paper, a multiple extended state observer (ESO) based control strategy is proposed to achieve stable tilt angle control for quadruped robots under the influence of disturbances and actuator failures. By treating the multiple legs as parallel control objects, more ESOs were added to improve the disturbance rejection ability of the linear active disturbance rejection control (LADRC). Correlation of interactive information about the legs is realized by the synthesis of multiple ESO information. Based on LADRC, this method has the advantages of easy parameter tuning, good robustness, and strong ability to cope with interference and fault conditions. A control system reliability evaluation method was proposed. The reliability and control performance of the multi-ESO based control system under leg stuck failure conditions were systematically analyzed. Simulation and experimental results for the level adjustment control system of a quadruped robot are provided to verify the disturbance rejection ability, feasibility and practicability of the proposed multi-ESO based control method.
PL
Złożoność algorytmów sterowania oraz ich brak odporności na zakłócenia i uszkodzenia to główne czynniki ograniczające pracę wielonożnych robotów mobilnych w bardziej złożonych środowiskach. W przedstawionym artykule zaproponowano strategię sterowania wykorzystującą kilka obserwatorów stanu rozszerzonego (ESO), która pozwala na uzyskanie stabilnego kąta pochylenia robota czworonożnego w warunkach zakłóceń i uszkodzeń siłowników. Traktując każdą z nóg jako równorzędny obiekt sterowania, dodano dodatkowe ESO, co pozwoliło na poprawienie zdolności algorytmu liniowego aktywnego tłumienia zakłóceń (LADRC) do kompensacji (tłumienia) tych ostatnich. Interaktywne informacje dotyczące poszczególnych nóg korelowano poprzez syntezę danych z ESO. Zaletami omawianej metody opartej na LADRC są: łatwość dostrajania parametrów, wysoka niezawodność oraz bardzo dobra zdolność do radzenia sobie z zakłóceniami i uszkodzeniami. Zaproponowano także metodę oceny niezawodności systemu sterowania. Analizowano niezawodność i wydajność systemu opartego na kilku ESO w warunkach awarii wywołanej zablokowaniem nóg robota. Przedstawiono wyniki badań symulacyjnych i eksperymentalnych systemu sterowania regulacją poziomu robota czworonożnego, które pozwalają zweryfikować zdolność proponowanej metody do tłumienia zakłóceń, a także możliwość jej praktycznego zastosowania.
Rocznik
Strony
42--51
Opis fizyczny
Bibliogr. 34 poz., rys., tab.
Twórcy
autor
  • School of Reliability and Systems Engineering Beihang University No.37 Xueyuan RD. Haidian, 100191, Beijing, China
autor
  • School of Reliability and Systems Engineering Beihang University No.37 Xueyuan RD. Haidian, 100191, Beijing, China
autor
  • School of Reliability and Systems Engineering Beihang University No.37 Xueyuan RD. Haidian, 100191, Beijing, China
  • xubinghui@buaa.edu.cn
autor
  • School of Reliability and Systems Engineering Beihang University No.37 Xueyuan RD. Haidian, 100191, Beijing, China
  • dezhenyang@buaa.edu.cn
  • School of Reliability and Systems Engineering Beihang University No.37 Xueyuan RD. Haidian, 100191, Beijing, China
Bibliografia
  • 1. Castaneda L, Luviano-Juarez A, Chairez I. Robust Trajectory Tracking of a Delta Robot Through Adaptive Active Disturbance Rejection Control. IEEE Transactions on Control Systems Technology 2015; 23(4): 1387-1398, https://doi.org/10.1109/TCST.2014.2367313.
  • 2. Christensen D, Schultz U, Stoy K. A distributed and morphology-independent strategy for adaptive locomotion in self-reconfigurable modular robots. Robotics & Autonomous Systems 2013; 61(9): 1021-1035, https://doi.org/10.1016/j.robot.2013.05.009.
  • 3. Cong Z. Distributed ESO based cooperative tracking control for high-order nonlinear multi-agent systems with lumped disturbance and application in multi flight simulators systems. Isa Transactions 2018; 74: 217-228, https://doi.org/10.1016/j.isatra.2018.01.020.
  • 4. Cui J, Zeng S, Ren Y, Chen X, Gao Z. On the robustness and reliability in the pose deformation system of mobile robots. IEEE Access 2018, https://doi.org/10.1109/ACCESS.2018.2835836.
  • 5. Cully A, Clune J, Tarapore D, Mouret J. Robots that can adapt like animals. Nature 2015; 521(7553): 503-507, https://doi.org/10.1038/nature14422.
  • 6. Du B, Wu S, Han S, Cui S. Application of Linear Active Disturbance Rejection Controller for Sensorless Control of Internal Permanent-Magnet Synchronous Motor. IEEE Transactions on Industrial Electronics 2016; 63(5): 3019-3027, https://doi.org/10.1109/TIE.2016.2518123.
  • 7. Falconi G, Heise C, Holzapfel F. Fault-tolerant position tracking of a hexacopter using an Extended State Observer. International Conference on Automation, Robotics and Applications. IEEE 2015; 550-556, https://doi.org/10.1109/ICARA.2015.7081207.
  • 8. Gonzalez-Prieto I, Duran M, Barrero F. Fault-Tolerant Control of Six-Phase Induction Motor Drives With Variable Current Injection. IEEE Transactions on Power Electronics 2017; 32(10): 7894-7903, https://doi.org/10.1109/TPEL.2016.2639070.
  • 9. Han J. From PID to active disturbance rejection control. IEEE Transactions on Industrial Electronics 2009; 56(3): 900-906, https://doi.org/10.1109/TIE.2008.2011621.
  • 10. Herbst G. Practical Active Disturbance Rejection Control: Bumpless Transfer, Rate Limitation, and Incremental Algorithm. IEEE Transactions on Industrial Electronics 2016; 63(3): 1754-1762, https://doi.org/10.1109/TIE.2015.2499168.
  • 11. Huang Y, Xue W. Active disturbance rejection control: Methodology and theoretical analysis. ISA Transactions 2014; 53(4): 963-976, https://doi.org/10.1016/j.isatra.2014.03.003.
  • 12. Kommuri S, Defoort M, Karimi H, Veluvolu K. A Robust Observer-Based Sensor Fault-Tolerant Control for PMSM in Electric Vehicles. IEEE Transactions on Industrial Electronics 2016; 63(12): 7671-7681, https://doi.org/10.1109/TIE.2016.2590993.
  • 13. Li D, Ding P, Gao Z. Fractional active disturbance rejection control. ISA Transactions 2016; 62: 109-119, https://doi.org/10.1016/j.isatra.2016.01.022.
  • 14. Li D, Li C, Gao Z, Jin Q. On active disturbance rejection in temperature regulation of the proton exchange membrane fuel cells. Journal of Power Sources 2015; 283: 452-463, https://doi.org/10.1016/j.jpowsour.2015.02.106.
  • 15. Li J, Xia Y, Qi X, Gao Z. On the Necessity, Scheme and Basis of the Linear-Nonlinear Switching in Active Disturbance Rejection Control. IEEE Transactions on Industrial Electronics 2017; 64(2): 1425-1435, https://doi.org/10.1109/TIE.2016.2611573.
  • 16. Liu F, Li Y, Cao Y, She J, Wu M. A Two-Layer Active Disturbance Rejection Controller Design for Load Frequency Control of Interconnected Power System. IEEE Transactions on Power Systems 2016; 31(4): 3320-3321, https://doi.org/10.1109/TPWRS.2015.2480005.
  • 17. Ma X, Sun F, Li H, He B. Neural-network-based integral sliding-mode tracking control of second-order multi-agent systems with unmatched disturbances and completely unknown dynamics. International Journal of Control, Automation and Systems 2017; 15(4): 1925-1935, https://doi.org/10.1007/s12555-016-0057-z.
  • 18. Madonski R, Kordasz M, Sauer P. Application of a disturbance-rejection controller for robotic-enhanced limb rehabilitation trainings. ISA Transactions 2014; 53(4): 899-908, https://doi.org/10.1016/j.isatra.2013.09.022.
  • 19. Marina S, Franc N, Gregor P. Sensors in proactive maintenance - a case of LTCC pressure sensors. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2018; 20(2): 267-272, https://doi.org/10.17531/ein.2018.2.12.
  • 20. Ramos G, Cortes-Romero J, Coral-Enriquez H. Spatial observer-based repetitive controller: An active disturbance rejection approach. Control Engineering Practice 2015; 42: 1-11, https://doi.org/10.1016/j.conengprac.2015.05.002.
  • 21. Ran M, Wang Q, Dong C. Stabilization of a class of nonlinear systems with actuator saturation via active disturbance rejection control. Automatica 2016; 63: 302-310, https://doi.org/10.1016/j.automatica.2015.10.010.
  • 22. Rymarczyk T, Klosowski G. Innovative methods of neural reconstruction for tomographic images in maintenance of tank industrial reactors. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21(2): 261-267, https://doi.org/10.17531/ein.2019.2.10.
  • 23. Song Y, Guo J. Neuro-Adaptive Fault-Tolerant Tracking Control of Lagrange Systems Pursuing Targets With Unknown Trajectory. IEEE Transactions on Industrial Electronics 2017; 64(5): 3913-3920, https://doi.org/10.1109/TIE.2016.2644606.
  • 24. Su X, Liu X, Song Y. Fault-Tolerant Control of Multi-area Power Systems via a Sliding-Mode Observer Technique. IEEE/ASME Transactions on Mechatronics 2018; 23(1): 38-47, https://doi.org/10.1109/TMECH.2017.2718109.
  • 25. Shen Q, Wang D, Zhu S, Poh E. Robust Control Allocation for Spacecraft Attitude Tracking Under Actuator Faults. IEEE Transactions on Control Systems Technology 2017; 25(3): 1068-1075, https://doi.org/10.1109/TCST.2016.2574763.
  • 26. Wu D, Chen K. Limit cycle analysis of active disturbance rejection control system with two nonlinearities. ISA Transactions 2014; 53(4):947-954, https://doi.org/10.1016/j.isatra.2014.03.001.
  • 27. Xiao B, Yin S. Velocity-Free Fault-Tolerant and Uncertainty Attenuation Control for a Class of Nonlinear Systems. IEEE Transactions on Industrial Electronics 2016; 63(7): 4400-4411, https://doi.org/10.1109/TIE.2016.2532284.
  • 28. Xu C, Li J, Zhang P, Mu L, Si X. ESO-based fault diagnosis and fault-tolerant for incipient actuator faults. Control and Decision Conference. IEEE 2013; 4359-4363.
  • 29. Xue W, Bai W, Yang S, Song K, Huang Y, Xie H. ADRC With Adaptive Extended State Observer and its Application to Air-Fuel Ratio Control in Gasoline Engines. IEEE Transactions on Industrial Electronics 2015; 62(9): 5847-5857, https://doi.org/10.1109/TIE.2015.2435004.
  • 30. Xue W, Huang Y. On performance analysis of ADRC for a class of MIMO lower-triangular nonlinear uncertain systems. ISA Transactions 2014; 53(4): 955-962, https://doi.org/10.1016/j.isatra.2014.02.002.
  • 31. Yang X, Cui J, Lao D, Li D, Chen J. Input Shaping enhanced Active Disturbance Rejection Control for a twin rotor multi-input multi-output system (TRMS). ISA Transactions 2016; 62: 287-298, https://doi.org/10.1016/j.isatra.2016.02.001.
  • 32. Yang Y, Huang H, Liu Y, Zhu S, Peng W. Reliability analysis of electrohydraulic servo valve suffering common cause failures. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2014; 16(3): 354-359.
  • 33. Yu B, Paassen A. Simulink and bond graph modeling of an air-conditioned room. Simulation Modelling Practice & Theory 2004; 12(1): 61-76, https://doi.org/10.1016/j.simpat.2003.12.001.
  • 34. Zhou Y, Huang Z, Liu W, Li H, Liao H. A distributed ESO based cooperative current-sharing strategy for parallel charging systems under disturbances. IEEE Energy Conversion Congress & Exposition 2016, https://doi.org/10.1109/ECCE.2016.7854675.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-26433175-46fa-45ef-8588-368f8aad52e2
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