Skip to main content
Log in

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

  • Original
  • Published:
Archive of Applied Mechanics Aims and scope Submit manuscript

This article has been updated

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

Change history

  • 13 January 2021

    Journal abbreviated title on top of the page has been corrected to “Arch Appl Mech”.

References

  1. 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.2019.03.007

    Article  Google Scholar 

  2. 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/s11044-019-09693-1

    Article  MathSciNet  MATH  Google Scholar 

  3. 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-w

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. 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/s00419-018-1472-9

    Article  Google Scholar 

  6. 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.2014.06.004

    Article  Google Scholar 

  7. 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.2018.10.011

    Article  Google Scholar 

  8. 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/s40064-016-3417-5

    Article  Google Scholar 

  9. 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.2017.08.011

    Article  Google Scholar 

  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/s00419-018-1491-6

    Article  Google Scholar 

  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.2017.08.1208

    Article  Google Scholar 

  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.2019.05.006

    Article  Google Scholar 

  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.2018.11.021

    Article  Google Scholar 

  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/10.1177/0959651817726477

    Article  Google Scholar 

  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/10.1177/1729881417705845

    Article  Google Scholar 

  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/s00419-018-1441-3

    Article  Google Scholar 

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

    Article  Google Scholar 

  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-z

    Article  Google Scholar 

  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.2018.02.009

    Article  Google Scholar 

  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/s12555-017-0087-1

    Article  Google Scholar 

  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/s00419-020-01687-2

    Article  Google Scholar 

  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/s12206-015-1245-0

    Article  Google Scholar 

  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.2015.06.005

    Article  Google Scholar 

  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/s40998-019-00228-0

    Article  Google Scholar 

  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/s13369-017-2917-0

    Article  Google Scholar 

  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.2020.104451

    Article  Google Scholar 

  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.2019.8895108

    Article  Google Scholar 

  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.2019.2909657

    Article  Google Scholar 

  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.2017.7946750

    Article  Google Scholar 

  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.2019.11.354

    Article  Google Scholar 

  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.1997.619069

    Article  Google Scholar 

  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.1997.619067

    Article  Google Scholar 

  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:1013918927148

    Article  MATH  Google Scholar 

  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/s12555-017-0487-2

    Article  Google Scholar 

  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/s12555-015-0409-0

    Article  Google Scholar 

  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-z

    Article  Google Scholar 

  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/s10846-018-0778-8

    Article  Google Scholar 

  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/s12541-019-00137-6

    Article  Google Scholar 

  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.2019.03.003

    Article  Google Scholar 

  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-y

    Article  Google Scholar 

  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.2016.09.004

    Article  Google Scholar 

  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/s11044-013-9403-6

    Article  MathSciNet  MATH  Google Scholar 

  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.2016.10.650

    Article  Google Scholar 

  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.2015.03.002

    Article  Google Scholar 

  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-1003.01079

    Article  Google Scholar 

  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.2017.08.892

    Article  Google Scholar 

  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.2009.01.015

    Article  MATH  Google Scholar 

  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.2016.07.688

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  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.2014.6893992

    Article  Google Scholar 

  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/s12555-010-0223-7

    Article  MATH  Google Scholar 

  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/10.1109/70.388778

    Article  Google Scholar 

  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/10.1109/70.56663

    Article  Google Scholar 

  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)

    Article  Google Scholar 

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

    Book  Google Scholar 

  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/10.1109/70.34777

    Article  Google Scholar 

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

    Google Scholar 

  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.2017.02.004

    Article  Google Scholar 

  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.2016.11.1.215

    Article  Google Scholar 

  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.2020.104508

    Article  Google Scholar 

  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/app10217482

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Vicerrectoría de Investigación, Desarrollo e Innovación of the Universidad de Santiago de Chile, Chile.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Claudio Urrea.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix A: Dynamic model for 5-DoF redundant manipulator robot

Appendix A: Dynamic model for 5-DoF redundant manipulator robot

The components of the inertia matrix M(q), the centrifugal and Coriolis force vector C(q,q̇) and the gravity vector G(q) [79] are presented as follows.

$${\mathbf{M}}\left( {\mathbf{q}} \right) = \left( {\begin{array}{*{20}c} {M_{11} } & {M_{12} } & {M_{13} } & {M_{14} } & {M_{15} } \\ {M_{21} } & {M_{22} } & {M_{23} } & {M_{24} } & {M_{25} } \\ {M_{31} } & {M_{32} } & {M_{33} } & {M_{34} } & {M_{35} } \\ {M_{41} } & {M_{42} } & {M_{43} } & {M_{44} } & {M_{45} } \\ {M_{51} } & {M_{52} } & {M_{53} } & {M_{54} } & {M_{55} } \\ \end{array} } \right)$$
(67)
$$M_{11} = m_{1} + m_{2} + m_{3} + m_{4} + m_{5}$$
(68)
$$\begin{aligned} M_{12} & = M_{21} = M_{13} = M_{31} = M_{14} = M_{41} \\ & = \cdots M_{25} = M_{52} = M_{35} = M_{53} = M_{45} = M_{54} = 0 \\ \end{aligned}$$
(69)
$$M_{15} = M_{51} = - m_{5}$$
(70)
$$\begin{aligned} M_{22} & = l_{c2}^{2} m_{2} + \left( {l_{2}^{2} + l_{c3}^{2} + 2l_{2} l_{c3} {\text{cos}}\theta_{3} } \right)m_{3} \\ & \quad + \cdots I_{2} + I_{3} + I_{4} + \left( {l_{2}^{2} + l_{3}^{2} + l_{c4}^{2} + 2l_{2} l_{3} {\text{cos}}\theta_{3} } \right. \\ & \quad + \cdots 2l_{3} l_{c4} {\text{cos}}\theta_{4} + \left. {2l_{2} l_{c4} {\text{cos}}\left( {\theta_{3} + \theta_{4} } \right)} \right)m_{4} \\ & \quad + \cdots \left( {l_{2}^{2} + l_{3}^{2} + l_{4}^{2} } \right. + 2l_{2} l_{3} {\text{cos}}\theta_{3} \\ & \quad + \cdots \left. {2l_{3} l_{4} {\text{cos}}\theta_{4} + 2l_{2} l_{4} {\text{cos}}\left( {\theta_{3} + \theta_{4} } \right)} \right)m_{5} \\ \end{aligned}$$
(71)
$$\begin{aligned} M_{23} & = M_{32} = \left( {l_{c3}^{2} + l_{2} l_{c3} {\text{cos}}\theta_{3} } \right)m_{3} + l_{3}^{2} m_{4} \\ & \quad + \cdots I_{3} + I_{4} + \left( {l_{c4}^{2} + l_{2} l_{3} {\text{cos}}\theta_{3} + 2l_{3} l_{c4} {\text{cos}}\theta_{4} } \right. \\ & \quad + \cdots \left. {l_{2} l_{c4} {\text{cos}}\left( {\theta_{3} + \theta_{4} } \right)} \right)m_{4} \\ & \quad + \cdots \left( {l_{3}^{2} + l_{4}^{2} + l_{2} l_{3} {\text{cos}}\theta_{3} + 2l_{3} l_{4} {\text{cos}}\theta_{4} } \right. \\ & \quad + \cdots \left. {l_{2} l_{4} {\text{cos}}\left( {\theta_{3} + \theta_{4} } \right)} \right)m_{5} \\ \end{aligned}$$
(72)
$$\begin{aligned} M_{24} & = M_{42} = \left( {l_{c4}^{2} + l_{3} l_{c4} {\text{cos}}\theta_{4} } \right. + \cdots \left. {l_{2} l_{c4} {\text{cos}}\left( {\theta_{3} + \theta_{4} } \right)} \right)m_{4} \\ & \quad + \cdots \left( {l_{4}^{2} + l_{3} l_{4} {\text{cos}}\theta_{4} + l_{2} l_{4} {\text{cos}}\left( {\theta_{3} + \theta_{4} } \right)} \right)m_{5} + I_{4} \\ \end{aligned}$$
(73)
$$\begin{aligned} M_{33} & = l_{c3}^{2} m_{3} + \left( {l_{3}^{2} + l_{c4}^{2} + 2l_{3} l_{c4} {\text{cos}}\theta_{4} } \right)m_{4} \\ & \quad + \cdots \left( {l_{3}^{2} + l_{4}^{2} + 2l_{3} l_{4} {\text{cos}}\theta_{4} } \right)m_{5} + I_{3} + I_{4} \\ \end{aligned}$$
(74)
$$M_{34} = M_{43} = \left( {l_{c4}^{2} + l_{3} l_{c4} {\text{cos}}\theta_{4} } \right)m_{4} + \cdots \left( {l_{4}^{2} + l_{3} l_{4} {\text{cos}}\theta_{4} } \right)m_{5} + I_{4}$$
(75)
$$M_{44} = l_{c4}^{2} m_{4} + l_{4}^{2} m_{5} + I_{4}$$
(76)
$$M_{55} = m_{5}$$
(77)
$${\mathbf{C}}\left( {{\mathbf{q}},{\dot{\mathbf{q}}}} \right) = \left( {\begin{array}{*{20}c} {C_{11} } & {C_{12} } & {C_{13} } & {C_{14} } & {C_{15} } \\ {C_{21} } & {C_{22} } & {C_{23} } & {C_{24} } & {C_{25} } \\ {C_{31} } & {C_{32} } & {C_{33} } & {C_{34} } & {C_{35} } \\ {C_{41} } & {C_{42} } & {C_{43} } & {C_{44} } & {C_{45} } \\ {C_{51} } & {C_{52} } & {C_{53} } & {C_{54} } & {C_{55} } \\ \end{array} } \right)$$
(78)
$$\begin{aligned} C_{11} & = C_{12} = C_{13} = C_{14} = C_{15} = C_{21} = C_{25} \\ & = \cdots C_{31} = C_{35} = C_{41} = C_{44} = C_{45} = C_{51} = C_{52} \\ & = \cdots C_{53} = C_{54} = C_{55} = 0 \\ \end{aligned}$$
(79)
$$\begin{aligned} C_{22} & = - \left( {m_{3} l_{2} l_{c3} + m_{4} l_{2} l_{3} + m_{5} l_{2} l_{3} } \right){\text{sin}}\theta_{3} \dot{\theta }_{3} \\ & \quad + \cdots - \left( {m_{4} l_{2} l_{c4} + m_{5} l_{2} l_{4} } \right){\text{sin}}\left( {\theta_{3} + \theta_{4} } \right)\left( {\dot{\theta }_{3} + \dot{\theta }_{4} } \right) \\ & \quad + \cdots - \left( {m_{4} l_{3} l_{c4} + m_{5} l_{3} l_{4} } \right){\text{sin}}\theta_{4} \dot{\theta }_{4} \\ \end{aligned}$$
(80)
$$\begin{aligned} C_{23} & = - \left( {m_{3} l_{2} l_{c3} + m_{4} l_{2} l_{3} + \cdots } \right.\left. {m_{5} l_{2} l_{3} } \right){\text{sin}}\theta_{3} \left( {\dot{\theta }_{2} + \dot{\theta }_{3} } \right) \\ & \quad + \cdots - \left( {m_{4} l_{2} l_{c4} + m_{5} l_{2} l_{4} } \right){\text{sin}}\left( {\theta_{3} + \theta_{4} } \right)\left( {\dot{\theta }_{2} + \dot{\theta }_{3} + \dot{\theta }_{4} } \right) \\ & \quad + \cdots - \left( {m_{4} l_{3} l_{c4} + m_{5} l_{3} l_{4} } \right){\text{sin}}\theta_{4} \dot{\theta }_{4} \\ \end{aligned}$$
(81)
$$\begin{aligned} C_{24} & = - \left( {m_{4} l_{2} l_{c4} + m_{5} l_{2} l_{4} } \right){\text{sin}}\left( {\theta_{3} + \theta_{4} } \right)\left( {\dot{\theta }_{2} + \dot{\theta }_{3} + \dot{\theta }_{4} } \right) \\ & \quad + \cdots - \left( {m_{4} l_{3} l_{c4} + m_{5} l_{3} l_{4} } \right){\text{sin}}\theta_{4} \left( {\dot{\theta }_{2} + \dot{\theta }_{3} + \dot{\theta }_{4} } \right) \\ \end{aligned}$$
(82)
$$\begin{aligned} C_{32} & = \left( {m_{3} l_{2} l_{c3} + m_{4} l_{2} l_{3} + m_{5} l_{2} l_{3} } \right){\text{sin}}\theta_{3} \dot{\theta }_{2} \\ & \quad + \cdots \left( {m_{4} l_{2} l_{c4} + m_{5} l_{2} l_{4} } \right){\text{sin}}\left( {\theta_{3} + \theta_{4} } \right)\dot{\theta }_{2} \\ & \quad + \cdots - \left( {m_{4} l_{3} l_{c4} + m_{5} l_{3} l_{4} } \right){\text{sin}}\theta_{4} \dot{\theta }_{4} \\ \end{aligned}$$
(83)
$$C_{33} = - \left( {m_{4} l_{3} l_{c4} + m_{5} l_{3} l_{4} } \right){\text{sin}}\theta_{4} \dot{\theta }_{4}$$
(84)
$$C_{34} = - \left( {m_{4} l_{3} l_{c4} + m_{5} l_{3} l_{4} } \right){\text{sin}}\theta_{4} \left( {\dot{\theta }_{2} + \dot{\theta }_{3} + \dot{\theta }_{4} } \right)$$
(85)
$$\begin{aligned} C_{42} & = \left( {m_{4} l_{2} l_{c4} + m_{5} l_{2} l_{4} } \right){\text{sin}}\left( {\theta_{3} + \theta_{4} } \right)\dot{\theta }_{2} \\ & \quad + \cdots \left( {m_{4} l_{3} l_{c4} + m_{5} l_{3} l_{4} } \right){\text{sin}}\theta_{4} \left( {\dot{\theta }_{2} + \dot{\theta }_{3} } \right) \\ \end{aligned}$$
(86)
$$C_{43} = \left( {m_{4} l_{3} l_{c4} + m_{5} l_{3} l_{4} } \right){\text{sin}}\theta_{4} \left( {\dot{\theta }_{2} + \dot{\theta }_{3} } \right)$$
(87)
$${\mathbf{G}}\left( {\mathbf{q}} \right) = \left( {\begin{array}{*{20}c} {\left( {m_{1} + m_{2} + m_{3} + m_{4} + m_{5} } \right)g} \\ 0 \\ 0 \\ 0 \\ { - m_{5} g} \\ \end{array} } \right)$$
(88)

where: m1, m2, m3, m4, m5 and l1, l2, l3, l4, l5 represent the mass and lengths of the first, second, third, fourth and fifth link, respectively, lc2, lc3, lc4 express the length of origin in the mass centers of the second, third and fourth link, and I2, I3 and I4 represent the inertia moments of the second, third and fourth link with respect to the z axis of the corresponding joint.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Urrea, C., Pascal, J. Design and validation of a dynamic parameter identification model for industrial manipulator robots. Arch Appl Mech 91, 1981–2007 (2021). https://doi.org/10.1007/s00419-020-01865-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00419-020-01865-2

Keywords

Navigation