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Archive of Applied Mechanics

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07.01.2021 | Original

Design and validation of a dynamic parameter identification model for industrial manipulator robots

verfasst von: Claudio Urrea, José Pascal

Erschienen in: Archive of Applied Mechanics | Ausgabe 5/2021

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Abstract

This article presents the design and validation of a regression model for the identification of dynamic parameters in manipulator robots. The model exhibits implementation advantages as it is based on the acquisition of position, speed and voltage data from the actuator in each joint rather than on the calculation of acceleration and torque. Actuators can be direct current and/or servomotor type. The regression model developed is simulated using MATLAB/Simulink software to identify the parameters of 2-DoF (Degrees of Freedom) and 5-DoF manipulator robots. Additionally, the model is experimentally validated on a real 5-DoF redundant manipulator robot. The identification model has great advantages in terms of implementation.

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Zhao, G., Zhang, P., Ma, G., Xiao, W.: System identification of the nonlinear residual errors of an industrial robot using massive measurements. Robot. Comput. Integr. Manuf. 59, 104–114 (2019). https://doi.org/10.1016/j.rcim.2019.03.007CrossRef

Zaare, S., Reza, M.: Adaptive sliding mode control of n flexible-joint robot manipulators in the presence of structured. Multibody Syst. Dyn. (2019). https://doi.org/10.1007/s11044-019-09693-1MathSciNetCrossRefMATH

Kim, S.H., Nam, E., Ha, T.I., Hwang, S.-H., Lee, J.H., Park, S.-H., Min, B.-K.: Robotic machining: a review of recent progress. Int. J. Precis. Eng. Manuf. (2019). https://doi.org/10.1007/s12541-019-00187-wCrossRef

Pfeiffer, F.: Motion spaces of machine–process combinations. Arch. Appl. Mech. 89, 2115–2132 (2019). https://doi.org/10.1007/s00419-019-01564-7CrossRef

Kumar, P., Pratiher, B.: Modal characterization with nonlinear behaviors of a two-link flexible manipulator. Arch. Appl. Mech. 89, 1201–1220 (2019). https://doi.org/10.1007/s00419-018-1472-9CrossRef

Jin, J., Gans, N.: Parameter identification for industrial robots with a fast and robust trajectory design approach. Robot. Comput. Integr. Manuf. 31, 21–29 (2015). https://doi.org/10.1016/j.rcim.2014.06.004CrossRef

Roldán, C., Campa, F.J., Paris, J., Kölling, T., Altuzarra, O., Corves, B.: Application of the principle of energy equivalence to obtain suitable models of parallel planar mechanisms for identification including friction parameters. A case study: 5R RePlaLink haptic mechanism. Mechatronics 56, 87–100 (2018). https://doi.org/10.1016/j.mechatronics.2018.10.011CrossRef

Li, K., Yang, W., Chan, K., Lin, P.: An optimization technique for identifying robot manipulator parameters under uncertainty. Springerplus 5, 1771 (2016). https://doi.org/10.1186/s40064-016-3417-5CrossRef

Gao, G., Sun, G., Na, J., Guo, Y., Wu, X.: Structural parameter identification for 6 DOF industrial robots. Mech. Syst. Signal Process. (2017). https://doi.org/10.1016/j.ymssp.2017.08.011CrossRef

10.

Pappalardo, C.M., Guida, D.: On the dynamics and control of underactuated nonholonomic mechanical systems and applications to mobile robots. Arch. Appl. Mech. 89, 669–698 (2019). https://doi.org/10.1007/s00419-018-1491-6CrossRef

11.

Stürz, Y., Affolter, L., Smith, R.: Parameter identification of the KUKA LBR IIWA robot including constraints on physical feasibility. IFAC-PapersOnLine 50, 6863–6868 (2017). https://doi.org/10.1016/j.ifacol.2017.08.1208CrossRef

12.

Libin, Z., Jiacai, W., Jiaoliao, C., Kang, C., Bangyang, L., Fang, X.: Dynamic modeling for a 6-DOF robot manipulator based on a centrosymmetric static friction model and whale genetic optimization algorithm. Adv. Eng. Softw. (2019). https://doi.org/10.1016/j.advengsoft.2019.05.006CrossRef

13.

Katsumata, T., Navarro, B., Bonnet, V., Fraisse, P., Crosnier, A., Venture, G.: Optimal exciting motion for fast robot identification Application to contact painting tasks with estimated external forces. Rob. Auton. Syst. 113, 149–159 (2019). https://doi.org/10.1016/j.robot.2018.11.021CrossRef

14.

Jiang, S., Jiang, M., Cao, Y., Hua, D., Wu, H., Ding, Y., Chen, B.: A typical dynamic parameter identification method of 6-degree-of-freedom industrial robot. Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng. 231, 740–752 (2017). https://doi.org/10.1177/0959651817726477CrossRef

15.

Zhang, W., Zhu, J., Gu, D.: Identification of robotic systems with hysteresis using Nonlinear AutoRegressive eXogenous input models. Int. J. Adv. Robot. Syst. 14, 1729881417705845 (2017). https://doi.org/10.1177/1729881417705845CrossRef

16.

Pappalardo, C.M., Guida, D.: A comparative study of the principal methods for the analytical formulation and the numerical solution of the equations of motion of rigid multibody systems. Arch. Appl. Mech. 88, 2153–2177 (2018). https://doi.org/10.1007/s00419-018-1441-3CrossRef

17.

Wu, J., Wang, J., You, Z.: An overview of dynamic parameter identification of robots. Robot. Comput. Integr. Manuf. 26, 414–419 (2010)CrossRef

18.

Hoang, N., Kang, H.: Observer-based dynamic parameter identification for wheeled mobile robots. Int. J. Precis. Eng. Manuf. 16, 1085–1093 (2015). https://doi.org/10.1007/s12541-015-0140-zCrossRef

19.

Villani, V., Pini, F., Leali, F., Secchi, C.: Survey on human–robot collaboration in industrial settings: safety, intuitive interfaces and applications. Mechatronics 55, 248–266 (2018). https://doi.org/10.1016/j.mechatronics.2018.02.009CrossRef

20.

Oh, K., Seo, J.: Inertial parameter estimation of an excavator with adaptive updating rule using performance analysis of Kalman filter. Int. J. Control Autom. Syst. 16, 1226–1238 (2018). https://doi.org/10.1007/s12555-017-0087-1CrossRef

21.

Burkus, E., Awrejcewicz, J., Odry, P.: A validation procedure to identify joint friction, reductor self-locking and gear backlash parameters. Arch. Appl. Mech. (2020). https://doi.org/10.1007/s00419-020-01687-2CrossRef

22.

Hollerbach, J., Khalil, W., Gautier, M.: Model identification. In: Springer Handbook of Robotics, pp. 113–138. Springer, Cham (2016)

23.

Baglioni, S., Cianetti, F., Braccesi, C., de Micheli, D.: Multibody modelling of N DOF robot arm assigned to milling manufacturing. Dynamic analysis and position errors evaluation. J. Mech. Sci. Technol. 30, 405–420 (2016). https://doi.org/10.1007/s12206-015-1245-0CrossRef

24.

Carbonari, L.: Simplified approach for dynamics estimation of a minor mobility parallel robot. Mechatronics 30, 76–84 (2015). https://doi.org/10.1016/j.mechatronics.2015.06.005CrossRef

25.

Dönmez, E., Kocamaz, A.F.: Design of mobile robot control infrastructure based on decision trees and adaptive potential area methods. Iran. J. Sci. Technol. Trans. Electr. Eng. 44, 431–448 (2020). https://doi.org/10.1007/s40998-019-00228-0CrossRef

26.

Dönmez, E., Kocamaz, A.F., Dirik, M.: A vision-based real-time mobile robot controller design based on Gaussian function for indoor environment. Arab. J. Sci. Eng. 43, 7127–7142 (2018). https://doi.org/10.1007/s13369-017-2917-0CrossRef

27.

Prado, Á.J., Torres-Torriti, M., Yuz, J., Auat Cheein, F.: Tube-based nonlinear model predictive control for autonomous skid-steer mobile robots with tire–terrain interactions. Control Eng. Pract. (2020). https://doi.org/10.1016/j.conengprac.2020.104451CrossRef

28.

Ege, M., Kucuk, S.: Design and dynamic model of a novel powered above knee prosthesis. Med. Technol. Congr. (2019). https://doi.org/10.1109/tiptekno.2019.8895108CrossRef

29.

He, Y., Mai, X., Cui, C., Gao, J., Yang, Z., Zhang, K., Chen, X., Chen, Y., Tang, H.: Dynamic modeling, simulation, and experimental verification of a wafer handling SCARA robot with decoupling servo control. IEEE Access 7, 47143–47153 (2019). https://doi.org/10.1109/ACCESS.2019.2909657CrossRef

30.

Hussain, Z., Azlan, N.Z.: KANE’s method for dynamic modeling. In: Proceedings: 2016 IEEE International Conference on Automatic Control and Intelligent Systems I2CACIS 2016, pp. 174–179 (2017). https://doi.org/10.1109/I2CACIS.2016.7885310

31.

Qian, L., Liu, H.H.T.: Dynamics and control of a quadrotor with a cable suspended payload. Can. Con. Electr. Comput. Eng. (2017). https://doi.org/10.1109/CCECE.2017.7946750CrossRef

32.

Kim, S., Kwon, S.: On the dynamic model of a two-wheeled inverted pendulum robot. 2014 11th International Conference on Ubiquitous Robots and Ambient Intelligence URAI 2014, pp. 145–148 (2014). https://doi.org/10.1109/URAI.2014.7057519

33.

Naing, S.Y., Rain, T.: Analysis of position and angular velocity of four-legged robot (Mini-Bot) from dynamic model using euler-lagrange method. In: 2019 International Conference on Industrial Engineering, Applications and Manufacturing ICIEAM 2019, pp. 1–4 (2019). https://doi.org/10.1109/ICIEAM.2019.8743046

34.

Záda, V., Belda, K.: Mathematical modeling of industrial robots based on Hamiltonian mechanics. In: 17th International Carpathian Control Conference (ICCC), pp. 813–818 (2016)

35.

Gimenez, J., Rosales, C., Carelli, R.: Port-Hamiltonian modelling of a differential drive mobile robot with reference velocities as inputs. In: 2015 16th Workshop on Information Processing and Control RPIC 2015, pp. 1–6 (2016). https://doi.org/10.1109/RPIC.2015.7497079

36.

Herrera, D., Gimenez, J., Carelli, R.: Port-Hamiltonian modelling of a car-like robot. 2015 16th Workshop on Information Processing and Control, RPIC 2015, pp. 1–6 (2016). https://doi.org/10.1109/RPIC.2015.7497067

37.

Park, K., Choi, Y.: System identification method for robotic manipulator based on dynamic momentum regressor. In: 2016 12th IEEE International Conference on Control and Automation (ICCA), pp. 755–760 (2016)

38.

Taghbalout, M., Antoine, J.F., Abba, G.: Experimental dynamic identification of a Yumi collaborative robot. IFAC-PapersOnLine. 52, 1168–1173 (2019). https://doi.org/10.1016/j.ifacol.2019.11.354CrossRef

39.

Gautier, M.: Dynamic identification of robots with power model. Proc. IEEE Int. Conf. Robot. Autom. 3, 1922–1927 (1997). https://doi.org/10.1109/robot.1997.619069CrossRef

40.

Reyes, F., Kelly, R.: On parameter identification of robot manipulators. Proc. IEEE Int. Conf. Robot. Autom. 3, 1910–1915 (1997). https://doi.org/10.1109/robot.1997.619067CrossRef

41.

Chan, S.P.: An efficient algorithm for identification of robot parameters including drive characteristics. J. Intell. Robot. Syst. Theory Appl. 32, 291–305 (2001). https://doi.org/10.1023/A:1013918927148CrossRefMATH

42.

Liu, W., Huo, X., Liu, J., Wang, L.: Parameter identification for a quadrotor helicopter using multivariable extremum seeking algorithm. Int. J. Control. Autom. Syst. 16, 1951–1961 (2018). https://doi.org/10.1007/s12555-017-0487-2CrossRef

43.

Ahsan, M., Choudhry, M.A.: System identification of an airship using trust region reflective least squares algorithm. Int. J. Control. Autom. Syst. 15, 1384–1393 (2017). https://doi.org/10.1007/s12555-015-0409-0CrossRef

44.

Miranda, R.: A new parameter identification algorithm for a class of second order nonlinear systems: an on-line closed-loop approach. Int. J. Control. Autom. Syst. 16, 1142–1155 (2018). https://doi.org/10.1007/s12555-017-0380-zCrossRef

45.

Afrough, M., Hanieh, A.A.: Identification of dynamic parameters and friction coefficients. J. Intell. Robot. Syst. 94, 3–13 (2019). https://doi.org/10.1007/s10846-018-0778-8CrossRef

46.

Seung, J., Yoo, S., Chong, K.: Experiments on state and unmeasured-parameter estimation of two degree-of-freedom system for precise control based on JAUKF. Int. J. Precis. Eng. Manuf. 20, 1159–1168 (2019). https://doi.org/10.1007/s12541-019-00137-6CrossRef

47.

El Zaatari, S., Marei, M., Li, W., Usman, Z.: Cobot programming for collaborative industrial tasks: an overview. Robot. Auton. Syst. 116, 162–180 (2019). https://doi.org/10.1016/j.robot.2019.03.003CrossRef

48.

Ekal, M., Ventura, R.: On the accuracy of inertial parameter estimation of a free-flying robot while grasping an object. J. Intell. Robot. Syst. Theory Appl. (2019). https://doi.org/10.1007/s10846-019-01040-yCrossRef

49.

Urrea, C., Pascal, J.: Design, simulation, comparison and evaluation of parameter identification methods for an industrial robot. Comput. Electr. Eng. 67, 791–806 (2018). https://doi.org/10.1016/j.compeleceng.2016.09.004CrossRef

50.

Nodem, D., Weber, W.: Automatic identification of the parameters of dynamics of industrial robots. In: 2016 11th France-Japan 9th Europe-Asia Congress on Mechatronics (MECATRONICS)/17th International Conference on Research and Education in Mechatronics (REM), pp. 321–326 (2016)

51.

Guo, Q., Gautier, M., Liu, D., Perruquetti, W.: Identification of robot dynamic parameters using Jacobi differentiator. In: 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), pp. 220–225 (2015)

52.

Choi, J., Yoon, J., Park, J., Kim, P.: A numerical algorithm to identify independent grouped parameters of robot manipulator for control. In: 2011 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 373–378 (2011)

53.

Briot, S., Gautier, M.: Global identification of joint drive gains and dynamic parameters of parallel robots. Multibody Syst. Dyn. 33, 3–26 (2015). https://doi.org/10.1007/s11044-013-9403-6MathSciNetCrossRefMATH

54.

Brunot, M., Janot, A., Carrillo, F., Gautier, M.: A separable prediction error method for robot identification. IFAC-PapersOnLine. 49, 487–492 (2016). https://doi.org/10.1016/j.ifacol.2016.10.650CrossRef

55.

Klimchik, A., Furet, B., Caro, S., Pashkevich, A.: Identification of the manipulator stiffness model parameters in industrial environment. Mech. Mach. Theory. 90, 1–22 (2015). https://doi.org/10.1016/j.mechmachtheory.2015.03.002CrossRef

56.

Jubien, A., Gautier, M., Janot, A.: Dynamic identification of the Kuka LWR robot using motor torques and joint torque sensors data. IFAC Proc. 47, 8391–8396 (2014). https://doi.org/10.3182/20140824-6-ZA-1003.01079CrossRef

57.

Yin, W., Sun, L., Wang, M., Liu, J., Chen, X.: Design and parameters identification of flexible joint robot. In: 2017 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 1297–1302 (2017)

58.

Brunot, M., Janot, A., Carrillo, F.: State space estimation method for the identification of an industrial robot arm. IFAC-PapersOnLine 50, 9815–9820 (2017). https://doi.org/10.1016/j.ifacol.2017.08.892CrossRef

59.

Alonso, H., Mendonça, T., Rocha, P.: Hopfield neural networks for on-line parameter estimation. Neural Netw. 22, 450–462 (2009). https://doi.org/10.1016/j.neunet.2009.01.015CrossRefMATH

60.

Mizuno, N., Nguyen, C.H.: Parameters identification of robot manipulator based on particle swarm optimization. In: 2017 13th IEEE International Conference on Control Automation (ICCA), pp. 307–312 (2017)

61.

Wang, F., Wang, Y., Li, J., Fang, W.: Kinematics Parameters Identification for IRB 1400 Using Improved Quantum Behaved Particle Swarm Optimization. In: Proceedings of the 2015 International Conference on Communications, Signal Processing, and Systems, pp. 881–890. Springer, Berlin (2016)

62.

West, C., Montazeri, A., Monk, S., Taylor, C.: A genetic algorithm approach for parameter optimization of a 7DOF robotic manipulator. IFAC-PapersOnLine 49, 1261–1266 (2016). https://doi.org/10.1016/j.ifacol.2016.07.688CrossRef

63.

Reyes, F.: Robótica: control de robots manipuladores. Alfaomega, Barcelona (2011)

64.

Barrientos, A., Pañín, L., Balaguer, C., Aracil, R.: Fundamnentos de robótica. McGraw-Hill, New York (2007)

65.

Urrea, C., Kern, J.: Characterization, simulation and implementation of a new dynamic model for a dc servomotor. IEEE Latin Am. Trans. 12, 997–1004 (2014). https://doi.org/10.1109/TLA.2014.6893992CrossRef

66.

la Hera, P., Rehman, B.U., Ortíz, D.: Electro-hydraulically actuated forestry manipulator: Modeling and Identification. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3399–3404 (2012)

67.

Radkhah, K., Kulic, D., Croft, E.: Dynamic parameter identification for the CRS A460 robot. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3842–3847 (2007)

68.

Achili, B., Daachi, B., Ali-Cherif, A., Amirat, Y.: A c5 parallel robot identification and control. Int. J. Control Autom. Syst. 8, 369–377 (2010). https://doi.org/10.1007/s12555-010-0223-7CrossRefMATH

69.

Bouabaz, K., Zhu, Q.: Improved numerical technique for industrial robots model reduction and identification. In: Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications ICIEA 2016, pp. 1032–1038 (2016). https://doi.org/10.1109/ICIEA.2016.7603734

70.

Lin, S.K.: Minimal linear combinations of the inertia parameters of a manipulator. IEEE Trans. Robot. Autom. 11, 360–373 (1995). https://doi.org/10.1109/70.388778CrossRef

71.

Pham, C., Gautier, M.: Essential parameter of robots. In: Proceedings of the 30th Conference on Decision and Control, pp. 2769–2774. IEEE, Brighton, UK (1991)

72.

Gautier, M., Venture, G.: Identification of standard dynamic parameters of robots with positive definite inertia matrix. In: IEEE International Conference on Intelligent Robots and Systems, pp. 5815–5820 (2013). https://doi.org/10.1109/IROS.2013.6697198

73.

Mayeda, H., Yoshida, K., Osuka, K.: Base parameters of manipulator dynamic models. IEEE Trans. Robot. Autom. 6, 312–321 (1990). https://doi.org/10.1109/70.56663CrossRef

74.

Díaz, M., Mata, V., Valera, A., Page, A.: A methodology for dynamic parameters identification of 3-DOF RPS parallel robots in terms of relevant parameters. Mech. Mach. Theory. 45, 1337–1356 (2010)CrossRef

75.

Kessman, K.: System Identification: An Introduction. Springer, London (2011)CrossRef

76.

Ha, I., Ko, M., Kwon, S.: An efficient estimation algorithm for the model parameters of robotic manipulators. IEEE Trans. Robot. Autom. 5, 386–394 (1989). https://doi.org/10.1109/70.34777CrossRef

77.

Urrea, C.: Fundamentos de robótica industrial: cinemática de manipuladores. Universidad de Santiago de Chile, Chile (2013)

78.

Urrea, C., Pascal, J.: Parameter identification methods for real redundant manipulators. J. Appl. Res. Technol. 15, 320–331 (2017). https://doi.org/10.1016/j.jart.2017.02.004CrossRef

79.

Urrea, C., Kern, J.: Trajectory tracking control of a real redundant manipulator of the SCARA type. J. Electr. Eng. Technol. 11, 215–226 (2016). https://doi.org/10.5370/JEET.2016.11.1.215CrossRef

80.

Urrea, C., Kern, J., López, R.: Fault-tolerant communication system based on convolutional code for the control of manipulator robots. Control Eng. Pract. 101, 104508 (2020). https://doi.org/10.1016/j.conengprac.2020.104508CrossRef

81.

Urrea, C., Kern, J., Alvarado, J.: Design and evaluation of a new fuzzy control algorithm applied to a manipulator robot. Appl. Sci. 10, 1–21 (2020). https://doi.org/10.3390/app10217482CrossRef

Titel: Design and validation of a dynamic parameter identification model for industrial manipulator robots
verfasst von: Claudio Urrea
José Pascal
Publikationsdatum: 07.01.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Archive of Applied Mechanics / Ausgabe 5/2021
Print ISSN: 0939-1533
Elektronische ISSN: 1432-0681
DOI: https://doi.org/10.1007/s00419-020-01865-2

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