On the boundedness of the Coriolis matrix for robot manipulators

https://doi.org/10.58571/CNCA.AMCA.2025.036

Resumen: Robust control design aims to alleviate uncertainties and disturbances, generally based on observers design. In this case, often it is required to know the robot dynamical model, which makes their application more complex for experimental implementations. A particular interest of this paper is to study the boundedness of the Coriolis matrix with the objective of setting conditions under which it can be neglected for the design of the controller or in the implementation of observers for robot manipulators. Theoretical conditions are proposed to bound the product C(q, q˙)q˙ based on motion planning for a determined smooth Cartesian trajectory. Numerical simulations using a three degrees of freedom robot manipulator validate our findings.

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Caballero-Mora, J., Portillo-Velez, R., Tejada, J., Ramirez-Neria, M. & Vasquez-Santacruz, J. (2025). On the boundedness of the Coriolis matrix for robot manipulators. Memorias del Congreso Nacional de Control Automático 2025, pp. 208-213. https://doi.org/10.58571/CNCA.AMCA.2025.036

• Craig, J.J. (2005). Introduction to Robotics: Mechanics and Control. Pearson Prentice Hall, Upper Saddle River, NJ, 3rd edition.
• Eftekharian, A.A. and Sayyaadi, H. (2006). Design of mixed fuzzy-ga controller for scara type robot. 2173 – 2178.
• From, P.J., Schjolberg, I., Gravdahl, J.T., Pettersen, K.Y., and Fossen, T.I. (2010). On the boundedness and skewsymmetric properties of the inertia and coriolis matrices for vehicle-manipulator systems. IFAC Proceedings Volumes, 43(16), 193–198. 7th IFAC Symposium on Intelligent Aut. Vehicles.
• Jin, M., Kang, S.H., Chang, P.H., and Lee, E. (2005). Nonlinear bang-bang impact control: A seamless control in all contact modes. volume 2005, 557 – 564.
• Kelly, R., Victor, S., and Antonio, L. (2005). Control of Robot Manipulators in Joint Space. Springer, London.
• Krishnamurthy, P., Khorrami, F., and Wang, Z. (2017). Robust adaptive nonlinear control for robotic manipulators with flexible joints.
• Mulero-Martínez, J.I. (2007). Uniform bounds of the coriolis/centripetal matrix of serial robot manipulators. IEEE Trans. on Robotics, 23(5), 1083–1089.
• Nagata, F. and Watanabe, K. (2011). Adaptive learning with large variability of teaching signals for neural networks and its application to motion control of an industrial robot. Int. Journal of Automation and Computing, 8(1), 54 – 61.
• Saab, S.S. and Ghanem, P. (2018). A multivariable stochastic tracking controller for robot manipulators without joint velocities. IEEE Trans. on Automatic Control, 63(8), 2481 – 2495.
• Siciliano, B., Sciavicco, L., Villani, L., and Oriolo, G. (2009). Robotics: modelling, planning and control. Springer.
• Spong, M.W., Hutchinson, S., and Vidyasagar, M. (2006). Robot Modeling and Control. John Wiley & Sons, Hoboken, NJ.