Tytuł artykułu
Treść / Zawartość
Pełne teksty:
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
The main impedance control schemes in the task space require accurate knowledge of the kinematics and dynamics of the robotic system to be controlled. In order to eliminate this dependence and preserve the structure of this kind of algorithms, this paper presents an adaptive impedance control approach to robot manipulators with kinematic and dynamic parametric uncertainty. The proposed scheme is an inverse dynamics control law that leads to the closed-loop system having a PD structure whose equilibrium point converges asymptotically to zero according to the formal stability analysis in the Lyapunov sense. In addition, the general structure of the scheme is composed of continuous functions and includes the modeling of most of the physical phenomena present in the dynamics of the robotic system. The main feature of this control scheme is that it allows precise path tracking in both free and constrained spaces (if the robot is in contact with the environment). The proper behavior of the closed-loop system is validated using a two degree-of-freedom robotic arm. For this benchmark good results were obtained and the control objective was achieved despite neglecting non modeled dynamics, such as viscous and Coulomb friction.
Rocznik
Tom
Strony
363--374
Opis fizyczny
Bibliogr. 41 poz., tab., wykr.
Twórcy
autor
- Faculty of Science, Autonomous University of San Luis Potosí, Av. Salvador Nava S/N, San Luis Potosí, SLP, 78290 Mexico
autor
- Faculty of Science, Autonomous University of San Luis Potosí, Av. Salvador Nava S/N, San Luis Potosí, SLP, 78290 Mexico
autor
- Faculty of Science, Autonomous University of San Luis Potosí, Av. Salvador Nava S/N, San Luis Potosí, SLP, 78290 Mexico
autor
- Ericsson, Av. 5 de Febrero 1351, Edificio Fresno, Zona Industrial Benito Juárez, Santiago de Querétaro, Qro., 76120 Mexico
Bibliografia
- [1] Anderson, R.J. and Spong, M.W. (1988). Hybrid impedance control of robotic manipulators, IEEE Journal on Robotics and Automation 4(5): 549–556.
- [2] Belter, D., Łabecki, P., Fankhauser, P. and Siegwart, R. (2016). RGB-D terrain perception and dense mapping for legged robots, International Journal of Applied Mathematics and Computer Science 26(1): 81–97, DOI: 10.1515/amcs-2016-0006.
- [3] Canudas, C., Siciliano, B. and Bastin, G. (1996). Theory of Robot Control, Springer, London.
- [4] Carelli, R. and Kelly, R. (1991). An adaptive impedance/force controller for robot manipulators, IEEE Transactions on Automatic Control 36(8): 967–971.
- [5] Chiaverini, S., Siciliano, B. and Villani, L. (1999). A survey of robot interaction control schemes with experimental comparison, IEEE/ASME Transactions on Mechatronics 4(3): 273–285.
- [6] Chien, M.-C. and Huang, A.-C. (2004). Adaptive impedance control of robot manipulators based on function approximation technique, Robotica 22(4): 395–403.
- [7] Dulęba, I. and Opałka, M. (2013). A comparison of jacobian-based methods of inverse kinematics for serial robot manipulators, International Journal of Applied Mathematics and Computer Science 23(2): 373–382, DOI: 10.2478/amcs-2013-0028.
- [8] Gribovskaya, E., Kheddar, A. and Billard, A. (2011). Motion learning and adaptive impedance for robot control during physical interaction with humans, Proceedings of the 2011 IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, pp. 4326–4332.
- [9] Hagn, U., Nickl, M., Jörg, S., Passig, G., Bahls, T., Nothhelfer, A., Hacker, F., Le-Tien, L., Albu-Schäffer, A. et al. (2008). The DLR MIRO: A versatile lightweight robot for surgical applications, Industrial Robot: An International Journal 35(4): 324–336.
- [10] Haninger, K., Lu, J. and Tomizuka, M. (2016). Robust impedance control with applications to a series-elastic actuated system, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, South Korea, pp. 5367–5372.
- [11] He, W., Dong, Y. and Sun, C. (2016). Adaptive neural impedance control of a robotic manipulator with input saturation, IEEE Transactions on Systems, Man, and Cybernetics: Systems 46(3): 334–344.
- [12] Hogan, N. (1985). Impedance control: An approach to manipulation: Part I—Theory, Part II—Implementation, Part III—Applications, ASME Journal of Dynamic Systems, Measurement, and Control 107(1): 1–24.
- [13] Horn, R.A. and Johnson, C.R. (2012). Matrix Analysis, Cambridge University Press, New York, NY.
- [14] Hussain, S., Xie, S.Q. and Jamwal, P.K. (2013). Adaptive impedance control of a robotic orthosis for gait rehabilitation, IEEE Transactions on Cybernetics 43(3): 1025–1034.
- [15] Jianbin, H., Zongwu, X., Minghe, J., Zainan, J. and Hong, L. (2009). Adaptive impedance-controlled manipulator based on collision detection, Chinese Journal of Aeronautics 22(1): 105–112.
- [16] Jiang, Z.-H. (2005). Impedance control of flexible robot arms with parametric uncertainties, Journal of Intelligent and Robotic Systems 42(2): 113–133.
- [17] Kang, S.H., Jin, M. and Chang, P.H. (2009). A solution to the accuracy/robustness dilemma in impedance control, IEEE/ASME Transactions on Mechatronics 14(3): 282–294.
- [18] Kelly, R., Santibáñez, V. and Loría, A. (2005). Control of Robot Manipulators in Joint Space, Springer-Verlag, London.
- [19] Khalil, H.K. (1996). Nonlinear Systems, Prentice-Hall, Upper Saddle River, NJ.
- [20] Li, Z., Huang, Z., He, W. and Su, C.-Y. (2017). Adaptive impedance control for an upper limb robotic exoskeleton using biological signals, IEEE Transactions on Industrial Electronics 64(2): 1664–1674.
- [21] Lu, W.-S. and Meng, Q.-H. (1991). Impedance control with adaptation for robotic manipulations, IEEE Transactions on Robotics and Automation 7(3): 408–415.
- [22] Marchal-Crespo, L. and Reinkensmeyer, D.J. (2009). Review of control strategies for robotic movement training after neurologic injury, Journal of Neuroengineering and Rehabilitation 6(20): 1–15.
- [23] Martínez, P.A., Castelán, M. and Arechavaleta, G. (2016). Vision based persistent localization of a humanoid robot for locomotion tasks, International Journal of Applied Mathematics and Computer Science 26(3): 669–682, DOI: 10.1515/amcs-2016-0046.
- [24] Mendoza, M., Bonilla, I., Reyes, F. and González-Galván, E. (2012). A Lyapunov-based design tool of impedance controllers for robot manipulators, Kybernetika 48(6): 1136–1155.
- [25] Michel, A.N., Hou, L. and Liu, D. (2008). Stability of Dynamical Systems, Birkhaüser, Boston, MA.
- [26] Pérez-Ibarra, J.C., Dos Santos, W.M., Krebs, H.I. and Siqueira, A.A. (2014). Adaptive impedance control for robot-aided rehabilitation of ankle movements, Proceedings of the 5th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, Sao Paulo, Brazil, pp. 664–669.
- [27] Rahimifard, S., Talebi, H. and Mohammadi, A.D. (2016). Impedance control of non-passive bilateral teleoperation systems with uncertain dynamics, Proceedings of the 24th Iranian Conference on Electrical Engineering (ICEE), Shiraz, Iran, pp. 1931–1936.
- [28] Reyes, F. and Kelly, R. (1997). Experimental evaluation of identification schemes on a direct drive robot, Robotica 15(05): 563–571.
- [29] Rodríguez-Liñán, M.C., Mendoza, M., Bonilla, I. and Chávez-Olivares, C.A. (2017). Saturating stiffness control of robot manipulators with bounded inputs, International Journal of Applied Mathematics and Computer Science 27(1): 79–90, DOI: 10.1515/amcs-2017-0006.
- [30] Rouche, N., Habets, P. and Laloy, M. (1977). Stability Theory by Lyapunov’s Direct Method, Springer, New York, NY.
- [31] Sciavicco, L. and Siciliano, B. (2000). Modelling and Control of Robot Manipulators, Springer, London.
- [32] Sharifi, M., Behzadipour, S. and Vossoughi, G. (2012). Model reference adaptive impedance control of rehabilitation robots in operational space, Proceedings of the 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), Rome, Italy, pp. 1698–1703.
- [33] Sharifi, M., Behzadipour, S. and Vossoughi, G. (2014). Nonlinear model reference adaptive impedance control for human–robot interactions, Control Engineering Practice 32(1): 9–27.
- [34] Slotine, J.-J.E., Li, W. (1991). Applied Nonlinear Control, Prentice-Hall, Englewood Cliffs, NJ.
- [35] Song, A., Pan, L., Xu, G. and Li, H. (2015). Adaptive motion control of arm rehabilitation robot based on impedance identification, Robotica 33(9): 1795–1812.
- [36] Spong, M., Hutchinson, S. and Vidyasagar, M. (2005). Robot Modeling and Control, Wiley, New York, NY.
- [37] Takegaki, M. and Arimoto, S. (1981). A new feedback method for dynamic control of manipulators, ASME Journal of Dynamic Systems, Measurement, and Control 103(2): 119–125.
- [38] Wang, H. and Xie, Y. (2009). Adaptive inverse dynamics control of robots with uncertain kinematics and dynamics, Automatica 45(9): 2114–2119.
- [39] Xu, G., Song, A. and Li, H. (2011). Adaptive impedance control for upper-limb rehabilitation robot using evolutionary dynamic recurrent fuzzy neural network, Journal of Intelligent & Robotic Systems 62(3): 501–525.
- [40] Yang, C., Chen, J. and Cheng, L. (2016). Neural learning enhanced teleoperation control of robots with uncertainties, Proceedings of the 9th International Conference on Human System Interactions (HSI), Portsmouth, UK, pp. 223–228.
- [41] Yarza, A., Santibanez, V. and Moreno-Valenzuela, J. (2013). An adaptive output feedback motion tracking controller for robot manipulators: Uniform global asymptotic stability and experimentation, International Journal of Applied Mathematics and Computer Science 23(3): 599–611, DOI: 10.2478/amcs-2013-0045.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-99fba577-9e25-496f-8393-b50170fd1aff