Avtomatika i TelemekhanikaIssue 12Identification of Piecewise Linear Parameters of Regression Models of Non-Stationary Deterministic Systems
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1. Kolyubin S., Shiriaev A., Jubien A. Refining Dynamics Identification for Co-Bots: Case Study on KUKA LWR4+ // 20th IFAC World Congress. 2017.
2. Li Y., Hannaford B. Gaussian Process Regression for Sensorless Grip Force Estimation of Cable-Driven Elongated Surgical Instruments // IEEE Robotics and Automation Lett. 2017. V. 2. No. 3. P. 1312–1319.
3. Zhao B., Nelson C.A. Sensorless Force Sensing for Minimally Invasive Surgery //
4. J. Medical Devices. 2015. 9 (4).
5. Eom K.S., Suh I.H., Chung W.K., Oh S.R. Disturbance Observer Based Force Control of robot manipulator without force sensor // Proc. IEEE Int. Conf. on Robotics and Automation. 1998. V. 4. P. 3012–3017.
6. Stolt A., Linderoth M., Robertsson A., Johansson R. Force Controlled Robotic Assembly without a Force Sensor // 2012 IEEE Int. Conf. on Robotics and Automation. 2012. P. 1538–1543.
7. Hyo-Sung Ahn, Yang Quan Chen. Time Periodical Adaptive Friction Compensation // 2004 IEEE Int. Conf. on Robotics and Biomimetics. 2004. P. 362–367.
8. Noorbakhsh M., Yazdizadeh A. Adaptive Friction Compensation in a Two-Link Planar Robot Manipulator Using a New Lyapunov-based Controller // IEEE ICCA. 2010. P. 2132–2137.
9. Bittencourt A.C., Axelsson P. Modeling and Experiment Design for Identification of Wear in a Robot Joint under Load and Temperature Uncertainties Based on Friction Data // IEEE/ASME Trans. Mechatronics. 2014. V. 19. No. 5. P. 1694–1706.
10. Andersson S., Soderberg A., Bjorklund S. Friction Models for Sliding Dry, Boundary and Mixed Lubricated Contacts // Tribol. Int. 2007. V. 40. No. 4. P. 580–587.
11. Verbert K.A.J., Tuth R., Babuˇska R. Adaptive Friction Compensation: A Globally Stable Approach // IEEE/ASME Trans. Mechatronics. 2016. V. 21. No. 1. P. 351–363.
12. Dunbar W.B., de Callafon R.A., Kosmatka J.B. Coulomb and Viscous Friction Fault Detection with Application to a Pneumatic Actuator // Proc. IEEE/ASME Int. Conf. on Advanced Intelligent Mechatronics. 2001. V. 2. P. 1239–1244.
13. Pyrkin A., Mancilla F., Ortega R., Bobtsov A., Aranovskiy S. Identification of the Current–Voltage Characteristic of Photovoltaic Arrays // 12th IFAC Workshop on Adaptation and Learning in Control and Signal Process. ALCOSP. 2016. V. 49. No. 13. P. 223–228.
14. Pyrkin A., Mancilla-David F., Ortega R., Bobtsov A., Aranovskiy S. Identification of Photovoltaic Arrays’ Maximum Power Extraction Point via Dynamic Regressor Extension and Mixing // Int. J. Adaptive Control Signal Process. 2017. V. 31. No. 9. P. 1337–1349.
15. Aranovskij S.V., Bobtsov A.A., Pyrkin A.A. Kaskadnaya reduktsiya v zadachakh identifikatsii // Nauch.-tekhnich. vestn. informatsionnykh tekhnologij, mekhaniki i optiki. 2012. № 3. C. 149–150.
16. L'yung L. Identifikatsiya sistem. Teoriya dlya pol'zovatelej. M.: Nauka, 1991.
17. Miroshnik I.V., Nikiforov V.O., Fradkov A.L. Nelinejnoe i adaptivnoe upravlenie slozhnymi dinamicheskimi sistemami. SPb.: Nauka, 2000.
18. Andrievskij B.R., Fradkov A.L. Izbrannye glavy teorii avtomaticheskogo upravleniya s primerami na yazyke MATLAB. SPb.: Nauka, 1999.
19. Sastry S., Bodson M. Adaptive Control: Stability, Convergence and Robustness. Englewood Cliffs, NJ: Prentice-Hall, 1989.
20. Aranovskiy S., Bobtsov A., Ortega R., Pyrkin A. Parameters Estimation via Dynamic Regressor Extension and Mixing // Amer. Control Conf. 2016. P. 6971–6976. doi: 10.1109/ACC.2016.7526771.
21. Aranovskiy S., Bobtsov A., Ortega R., Pyrkin A. Performance Enhancement of Parameter Estimators via Dynamic Regressor Extension and Mixing // IEEE Trans. Automat. Control. 2016. V. 62. No. 7. P. 3546–3550.
22. Pyrkin A.A., Bobtsov A.A., Vedyakov A.A., Kolyubin S.A. Estimation of Polyharmonic Signal Parameters // Autom. Remote Control. 2015. V. 76. No. 8. P. 1400–1416.
