Skip to main content Accessibility help
×
Home

Tracking Control of Electrically Driven Robots Using a Model-free Observer

  • Alireza Izadbakhsh (a1), Saeed Khorashadizadeh (a2) and Payam Kheirkhahan (a1)

Summary

This paper presents a robust tracking controller for electrically driven robots, without the need for velocity measurements of joint variables. Many observers require the system dynamics or nominal models, while a model-free observer is presented in this paper. The novelty of this paper is presenting a new observer–controller structure based on function approximation techniques and Stone–Weierstrass theorem using differential equations. In fact, it is assumed that the lumped uncertainty can be modeled by linear differential equations. Then, using Stone–Weierstrass theorem, it is verified that these differential equations are universal approximators. The advantage of proposed approach in comparison with previous related works is simplicity and reducing the dimensions of regressor matrices without the need for any information of the systems’ dynamic. Simulation results on a 6-degrees of freedom robot manipulator driven by geared permanent magnet DC motors indicate the satisfactory performance of the proposed method in overcoming uncertainties and reducing the tracking error. To evaluate the performance of proposed controller in practical implementations, experimental results on an SCARA manipulator are presented.

Copyright

Corresponding author

*Corresponding author. E-mail: izadbakhsh_alireza@hotmail.com

References

Hide All
1.Van Den Berg, J., Abbeel, P. and Goldberg, K., “LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information,” Int. J. Rob. Res. 30, 895913 (2011).10.1177/0278364911406562
2.Berenson, D., Abbeel, P. and Goldberg, K., “A robot path planning framework that learns from experience,” Rob. Autom. IEEE Int. Conf. 36713678 (2012).
3.Schulman, J., Duan, Y., Ho, J., Lee, A., Awwal, I., Bradlow, H., Pan, J., Patil, S., Goldberg, K. and Abbeel, P., “Motion planning with sequential convex optimization and convex collision checking,” Int. J. Rob. Res. 33, 12511270 (2014).10.1177/0278364914528132
4.Srivastava, S., Fang, E., Riano, L., Chitnis, R., Russell, S. and Abbeel, P., “Combined task and motion planning through an extensible planner-independent interface layer,” IEEE Int. Conf. Rob. Autom. 639646 (2014).
5.Abbeel, P., Dolgov, D., Ng, A. Y. and Thrun, S., “Apprenticeship learning formotion planning with application to parking lot navigation,” IEEE/RSJ Int. Conf. Intell. Rob. Syst. 10831090 (2008).
6.Shang, Y., “Resilient multiscale coordination control against adversarial nodes,” Energies 11, 117 (2018). doi:10.3390/en11071844
7.Schulman, J., Ho, J., Lee, A. X., Awwal, I., Bradlow, H. and Abbeel, P., “Finding locally optimal, collision-free trajectories with sequential convex optimization,” Rob. Sci. Syst. 9, 110 (2013).
8.Ham, W. C., “Adaptive control based on explicit model of robot manipulators,” IEEE Trans. Autom. Control 38, 654658 (1993).
9.Izadbakhsh, A. and Kheirkhahan, P., “On the voltage-based control of robot manipulators revisited,” Int. J. Control Autom. Syst. 16(4), 18871894 (2018).10.1007/s12555-017-0035-0
10.Wang, L. X., A Course in Fuzzy Systems and Control, Englewood Cliffs (Prentice-Hall, NJ, 1996).
11.Guzmán, S. P., Valenzuela, J. M. and Santibanez, V., “Adaptive neural network motion control of manipulators with experimental evaluations,” Sci. World J. 113 (2014). doi:10.1155/2014/694706
12.Wang, L., Chai, T. and Zhai, L., “Neural network based terminal sliding-mode control of robotic manipulators including actuator dynamics,” IEEE Trans. Ind. Electron. 56, 32963304 (2009).10.1109/TIE.2008.2011350
13.Peng, J., Wang, J. and Wang, Y., “Neural network based robust hybrid control for robotic system: An H 8 approach,” Nonlinear Dyn. 65, 421431 (2011).10.1007/s11071-010-9902-4
14.Fallah Ghavidel, H. and Akbarzadeh Kalat, A., “Observer-based hybrid adaptive fuzzy control for affine and nonaffine uncertain nonlinear systems,” Neural Comput. Appl. 30, 11871202 (2018).10.1007/s00521-016-2732-7
15.Wai, R. J. and Muthusamy, R., “Fuzzy-neural-network inherited sliding-mode control for robot manipulator including actuator dynamics,” IEEE Trans. Neural Networks Learn. Syst. 24, 274287 (2013).
16.Izadbakhsh, A. and Kheirkhahan, P., “An alternative stability proof for “Adaptive Type-2 fuzzy estimation of uncertainties in the control of electrically flexible-joint robots”,” J. Vibr. Control. doi:10.1177/1077546318802694
17.Pan, Y. and Er, M. J., “Enhanced adaptive fuzzy control with optimal approximation error convergence,” IEEE Trans. Fuzzy Syst. 21, 11231132 (2013).10.1109/TFUZZ.2013.2244899
18.Khorashadizadeh, S. and Fateh, M. M., “Robust task-space control of robot manipulators using Legendre polynomials for uncertainty estimation,” Nonlinear Dyn. 79, 11511161 (2015).10.1007/s11071-014-1730-5
19.Izadbakhsh, A., “Robust control design for rigid-link flexible-joint electrically driven robot subjected to constraint: theory and experimental verification,” Nonlinear Dyn. 85, 751765 (2016).10.1007/s11071-016-2720-6
20.Izadbakhsh, A., “FAT-based robust adaptive control of electrically driven robots without velocity measurements,” Nonlinear Dyn. 89, 289304 (2017).10.1007/s11071-017-3454-9
21.Gole, N., Gole, A., Barra, K. and Bouktir, T., “Observer-based adaptive control of robot manipulators: Fuzzy systems approach,” Appl. Soft Comput. 8, 778787 (2008).10.1016/j.asoc.2007.05.011
22.Talole, S. E., Kolhe, J. P. and Phadke, S. B., “Extended-state-observer-based control of flexible-joint system with experimental validation,” IEEE Trans. Ind. Electron. 57, 14111419 (2010).10.1109/TIE.2009.2029528
23.Wei, X. and Guo, L., “Composite disturbance-observer-based control and terminal sliding mode control for non-linear systems with disturbances,” Int. J. Control 82, 10821098 (2009).10.1080/00207170802455339
24.Alvarez, J., Rosas, D. and Pena, J., “Analog implementation of a robust control strategy for mechanical systems,” IEEE Trans. Ind. Electron. 56, 33773385 (2009).10.1109/TIE.2009.2020706
25.Sira-Ramírez, H., López-Uribe, C. andVelasco-Villa, M., “Linear observer-based active disturbance rejection control of the omnidirectional mobile robot,” Asian J. Control 15, 5163 (2013).10.1002/asjc.523
26.Cui, R., Chen, L., Yang, C. and Chen, M., “Extended state observer-based integral sliding mode control for an underwater robot with unknown disturbances and uncertain nonlinearities,” IEEE Trans. Ind. Electron. 64(8), 67856795 (2017).10.1109/TIE.2017.2694410
27.Peng, Z. and Wang, J., “Output-feedback path-following control of autonomous underwater vehicles based on an extended state observer and projection neural networks,” IEEE Trans. Syst. Man Cybern. Syst. 48(4), 535544 (2018).10.1109/TSMC.2017.2697447
28.Zhao, L., Li, Q., Liu, B. and Cheng, H., “Trajectory tracking control of a one degree of freedom manipulator based on a switched sliding mode controller with a novel extended state observer framework,” IEEE Trans. Syst. Man Cybern. Syst. 19 (2018).
29.Wang, H., Li, S., Lan, Q., Zhao, Z. and Zhou, X., “Continuous terminal sliding mode control with extended state observer for PMSM speed regulation system,” Trans. Inst. Meas. Control 39(8), 11951204 (2017).10.1177/0142331216630361
30.Wang, S., Ren, X., Na, J. and Zeng, T., “Extended-state-observer-based funnel control for nonlinear servomechanisms with prescribed tracking performance,” IEEE Trans. Autom. Sci. Eng. 14(1), 98108 (2017).10.1109/TASE.2016.2618010
31.Peng, Z. and Wang, J., “Output-feedback path-following control of autonomous underwater vehicles based on an extended state observer and projection neural networks,” IEEE Trans. Syst. Man Cybern. Syst. 48(4), 535544 (2018).10.1109/TSMC.2017.2697447
32.Muller, P. C. and Ackermann, J., “Nichtlineare regelung von elastischen robotern,” In: VDI-Berichte 598, Steuerung und Regelung von Roboter (Springer-Verlag, Berlin, Germany, 1986) pp. 321333.
33.Nakao, M., Ohnishi, K. and Miyachi, K., “Robust decentralized joint control based on interference estimation,” IEEE Int. Conf. Rob. Autom. 4, 326333 (1987).
34.Kemf, C. J. and Kobayashi, S., “Disturbance observer and feedforward design for a high-speed direct-drive positioning table,” IEEE Trans. Contr. Syst. Technol. 7, 513526 (1999).10.1109/87.784416
35.Huang, Y. H. and Messner, W., “A novel disturbance observer design for magnetic hard drive servo system with rotary actuator,” IEEE Trans. Magn. 4, 18921894 (1998).10.1109/20.706734
36.Ishikawa, J. and Tomizuka, M., “Pivot friction compensation using an accelerometer and a disturbance observer for hard disk,” IEEE/ASME Trans. Mechatron. 3, 194201 (1998).10.1109/3516.712115
37.Mohammadi, A., Tavakoli, M., Marquez, H. J. and Hashemzadeh, F., “Nonlinear disturbance observer design for robotic manipulators,” Control Eng. Pract. 21, 253267 (2013).10.1016/j.conengprac.2012.10.008
38.Li, Z., Su, C. Y., Wang, L., Chen, Z. and Chai, T., “Nonlinear disturbance observer-based control design for a robotic exoskeleton incorporating fuzzy approximation,” IEEE Trans. Ind. Electron. 62(9), 57635775 (2015).10.1109/TIE.2015.2447498
39.Chu, Z., Cui, J. and Sun, F., “Fuzzy adaptive disturbance-observer-based robust tracking control of electrically driven free-floating space manipulator,” IEEE Syst. J. 8, 343352 (2014).10.1109/JSYST.2012.2220171
40.Chen, W. H., Yang, J., Guo, L. and Li, S., “Disturbance-observer-based control and related methods—An overview,” IEEE Trans. Ind. Electron. 63, 10831095 (2016).10.1109/TIE.2015.2478397
41.Tong, S. and Li, Y., “Observer-based fuzzy adaptive control for strict-feedback nonlinear systems,” Fuzzy Sets Syst. 160, 17491764 (2009).10.1016/j.fss.2008.09.004
42.Cheah, C. C., Liu, C. and Slotine, J. J. E., “Adaptive Jacobian tracking control of robots with uncertainties in kinematic, dynamic and actuator models,” IEEE Trans. Autom. Control 51, 10241029 (2006).10.1109/TAC.2006.876943
43.Izadbakhsh, A., “A note on the “nonlinear control of electrical flexible-joint robots”,” Nonlinear Dyn. 89, 27532767 (2017).10.1007/s11071-017-3623-x
44.Izadbakhsh, A., “Robust adaptive control of voltage saturated flexible joint robots with experimental evaluations,” AUT J. Model. Simul. 50(1), 3138 (2018).
45.Chen, W. H., “Disturbance observer based control for nonlinear systems,” IEEE/ASME Trans. Mechatron. 9, 706710 (2004).10.1109/TMECH.2004.839034
46.Spong, M. W., Hutchinson, S. and Vidyasagar, M., Robot Modelling and Control (Wiley, Hoboken, 2006).
47.Izadbakhsh, A. and Fateh, M. M., “Real-time robust adaptive control of robots subjected to actuator voltage constraint,” Nonlinear Dyn. 78, 19992014 (2014).10.1007/s11071-014-1574-z
48.Izadbakhsh, A. and Khorashadizadeh, S., “Robust task-space control of robot manipulators using differential equations for uncertainty estimation,” Robotica 35(9), 19231938 (2017).10.1017/S0263574716000588
49.Qu, Z. and Dawson, D. M., Robust Tracking Control of Robot Manipulators (IEEE Press, Inc., New York, 1996).
50.Izadbakhsh, A. and Rafiei, S. M. R., “Endpoint perfect tracking control of robots - A robust non inversionbased approach,” Int. J. Control Autom. Syst. 7, 888898 (2009).10.1007/s12555-009-0603-z
51.Izadbakhsh, A., Akbarzadeh Kalat, A., Fateh, M. M. and Rafiei, S. M. R., “A robust anti-windup control design for electrically driven robots–Theory and experiment,” Int. J. Control Autom. Syst. 9, 10051012 (2011).10.1007/s12555-011-0524-5
52.Izadbakhsh, A. and Khorashadizadeh, S., “Robust impedance control of robot manipulators using differential equations as universal approximator,” Int. J. Control. 91(10), 21702186 (2018).10.1080/00207179.2017.1336669
53.Izadbakhsh, A., “Closed-Form Dynamic Model of PUMA560 Robot Arm,” Proceedings of the 4th International Conference on Autonomous Robots and Agents (2009) pp. 675680.
54.Corke, P., “The unimation puma servo system,” CSIRO Div. Manuf. Technol. MTM-226, 154 (1994).
55.Corke, P. and Armstrong-Helouvry, B., “A search for consensus among model parameters reported for the PUMA560 robot,” IEEE Int. Conf. Rob. Autom. 2, 16081613 (1994).
56.Pratap, B. and Purwar, S., “Real-time implementation of neuro adaptive observer-based robust backstepping controller for twin rotor control system,” J. Control Autom. Electr. Syst. 25, 137150 (2014).10.1007/s40313-013-0098-y
57.Purwar, S., Kar, I. N. and Jha, A. N., “Adaptive output feedback tracking control of robot manipulators using position measurements only,” Expert Syst. Appl. 34, 27892798 (2008).10.1016/j.eswa.2007.05.030
58.Patra, J. C. and Kot, A. C., “Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks,” IEEE Trans. Syst. Man Cybern. Part B 32, 505511 (2002).10.1109/TSMCB.2002.1018769
59.Jabbari Asl, H. and Janabi-sharifi, F., “Adaptive neural network control of cable-driven parallel robots with input saturation,” Eng. Appl. Artif. Intell. 65, 252260 (2017).10.1016/j.engappai.2017.05.011
60.Jabbari Asl, H. and Yoon, J., “Robust trajectory tracking control of cable-driven parallel robots,” Nonlinear Dyn. 89(4), 27692784 (2017).10.1007/s11071-017-3624-9
61.Shang, Y., “On the delayed scaled consensus problems,” Appl. Sci. 7(7), 713 (2017).10.3390/app7070713

Keywords

Tracking Control of Electrically Driven Robots Using a Model-free Observer

  • Alireza Izadbakhsh (a1), Saeed Khorashadizadeh (a2) and Payam Kheirkhahan (a1)

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed