Super-Twisting Control for trajectory tracking of a four-degree of freedom anthropomorphic robot manipulator
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Keywords

motion control
sliding mode
anthropomorphic robot
manipulator robot
robustness
trajectory tracking
simulation
super-twisting
tuning
disturbances
algorithms
kinematics
control
geometric approach
proportional derivative control de movimiento
modos deslizantes
robot antropomórfico
robot manipulador
robustez
seguimiento de trayectoria
simulación
super-twisting
sintonización
perturbaciones
algoritmos
cinemática
control
enfoque geométrico
proporcional derivativo

How to Cite

Ibarra Ontiveros, E. G., Lozano Hernández, Y., Enríquez Rocha, M. A., Galván Guerra, R., & Maya Rodríguez, M. C. (2022). Super-Twisting Control for trajectory tracking of a four-degree of freedom anthropomorphic robot manipulator. Nova Scientia, 14(28). https://doi.org/10.21640/ns.v14i28.2723

Abstract

This work solves the regulation and tracking trajectories tasks for four degrees of freedom anthropomorphic robot manipulators. Two controllers are considered: a Super-Twisting controller (ST) and a Proportional Derivative with dynamics compensation (PD+) control. This comparison is carried out through numeric simulation of the dynamic model in the presence of disturbances using Matlab-Simulink software. Also, the tuning procedure of each controller is shown, as well as the stability criteria used for each case. The tunning of the ST controller is done considering the effects produced by an unknown Lipschitz disturbance; this guarantees robustness against this kind of disturbance. The results of the ST controller show the rejection of the disturbance, allowing the correct trajectory tracking. An algorithm based on the inverse kinematics solution is used to generate trajectories and their interpretation in generalized coordinates corresponding to the manipulator’s joint positions obtained through the geometric approach. In addition, we show the workspace, the manipulator parameterization, and the manipulator dynamic model through the Euler-Lagrange motion equations.

 

https://doi.org/10.21640/ns.v14i28.2723
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References

Amer, A. F., Sallam, E. A., & Elawady, W. M. (2011). Adaptive fuzzy sliding mode control using supervisory fuzzy Control for 3 DOF planar robot manipulators. Applied Soft Computing, 11(8), 4943-4953.

Arimoto, S. (1986). Stability and robustness of PD feedback control with gravity compensation for robot manipulator. Robotics: Theory and Applications-DSC, 3, 67-72.

Chua, P. Y., Ilschner, T., & Caldwell, D. G. (2003). Robotic manipulation of food products–a review. Industrial Robot, 30(4), 345-354. https://doi.org/10.1108/01439910310479612

Codourey, A. (1998). Dynamic modeling of parallel robots for computed-torque control implementation. The International Journal of Robotics Research, 17(12), 1325-1336. https://doi.org/10.1177%2F027836499801701205

Corke, P. (2017). Robotics, vision and Control: fundamental algorithms in MATLAB® second, completely revised (Vol. 118). Springer.

Corradini, M. L., Fossi, V., Giantomassi, A., Ippoliti, G., Longhi, S., & Orlando, G. (2012). Minimal resource allocating networks for discrete time sliding mode control of robotic manipulators. IEEE Transactions on Industrial Informatics, 8(4), 733-745.

González Rodríguez, A., Pineda Ortega, M., & Soberanes Leal, D. M. (2007). Seguimiento adaptativo de trayectorias con convergencia en tiempo finito de un robot antropomórfico virtual de tres grados de libertad. [Tesis para obtener el grado de Licenciado en Ciencias Computacionales. UAEH]. http://dgsa.uaeh.edu.mx:8080/bibliotecadigital/bitstream/handle/231104/163/robot%20antropomorfico%20virtual.pdf?sequence=1&isAllowed=y

Han, S., Wang, H., Tian, Y., & Christov, N. (2020). Time-delay estimation based computed torque control with robust adaptive RBF neural network compensator for a rehabilitation exoskeleton. ISA transactions, 97, 171-181.

Hernandez, D., Yu, W., & Moreno-Armendariz, M. A. (2011). Neural PD control with second-order sliding mode compensation for robot manipulators. In The 2011 International Joint Conference on Neural Networks (pp. 2395-2402). IEEE.

Ibarra, E. G., Enriquez, M. A., Lozano, Y., Galván-Guerra, R., & Maya, M. C. (2019). Modelado cinemático y dinámico de un manipulador antropomórfico de cuatro grados de libertad. Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI, 7(Especial), 116-123.

Jaso, A. & Rosas, D. I. (2015). Brazo robótico planar con dos grados de libertad. [Ingeniería en Comunicaciones y Electrónica. Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica].

Kelly, R., Davila, V. S., & Perez, J. A. L. (2006). Control of robot manipulators in joint space. Springer Science & Business Media.

Lee, M., & Choi, H. S. (2000). A robust neural controller for underwater robot manipulators. IEEE Transactions on Neural Networks, 11(6), 1465-1470. https://doi.org/10.1109/72.883478

Levant, A. (1993). Sliding order and sliding accuracy in sliding mode control. International Journal of Control, 58(6), 1247-1263.

Levant, A. (1998). Robust exact differentiation via sliding mode technique. Automatica, 34(3), 379-384.

Markert, J., & Merk, G. (2009). U.S. Patent No. 7,603,927. Washington, DC: U.S. Patent and Trademark Office.

Meike, D., Pellicciari, M., & Berselli, G. (2013). Energy efficient use of multirobot production lines in the automotive industry: Detailed system modeling and optimization. IEEE Transactions on Automation Science and Engineering, 11(3), 798-809.

Meza, A. (2003). Observadores difusos y control adaptable difuso basado en observadores. Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional.

Middletone, R. H., & Goodwin, G. C. (1986). Adaptive computed torque control for rigid link manipulators. In 1986 25th IEEE Conference on Decision and Control (pp. 68-73). IEEE.

Moreno, J. A. (2009). A linear framework for the robust stability analysis of a generalized super-twisting algorithm. In 2009 6th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) (pp. 1-6). IEEE.

Orozco-Soto, S. M., & Fernández, J. C. R. (2015). Control par calculado difuso basado en pasividad para seguimiento de trayectorias de robots manipuladores. Research in Computing Science, 91, 131-141.

Park, J. H., & Kim, K. D. (1998). Biped robot walking using gravity-compensated inverted pendulum mode and computed torque control. In Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No. 98CH36146) (Vol. 4, pp. 3528-3533). IEEE.

Piltan, F., & Sulaiman, N. B. (2012). Review of sliding mode control of robotic manipulator. World Applied Sciences Journal, 18(12), 1855-1869.

Reyes Cortes, F. (2011). Robótica: control de robots manipuladores (No. 004.896 629,892).

Román, M. A. (2012). Control de un robot manipulador mediante la interpretación de ondas cerebrales. [Maestría en Tecnología de Cómputo. Instituto Politécnico Nacional, Centro de Innovación y Desarrollo Tecnológico en Cómputo]. https://tesis.ipn.mx/handle/123456789/21508?show=full

Seeber, R., & Horn, M. (2017). Stability proof for a well-established super-twisting parameter setting. Automatica, 84, 241-243.

Shepherd, S., & Buchstab, A. (2014). Kuka robots on-site. In Robotic Fabrication in Architecture, Art and Design 2014 (pp. 373-380). Springer, Cham.

Shtessel, Y., Edwards, C., Fridman, L., & Levant, A. (2014). Sliding mode control and observation. Springer New York. https://link.springer.com/book/10.1007/978-0-8176-4893-0

Sira-Ramírez, H. (2015). Sliding mode control: the delta-sigma modulation approach. Birkhäuser.

Slotine, J. J., & Weiping, L. (1988). Adaptive manipulator control: A case study. IEEE transactions on automatic Control, 33(11), 995-1003.

Sugeno, M., & Takagi, T. (1993). Fuzzy identification of systems and its applications to modelling and Control. Readings in Fuzzy Sets for Intelligent Systems, 15(1), 387-403.

Trejo Zúñiga, E. C. (2018). Estudios de controladores por modos deslizantes de orden superior en robots manipuladores. [Maestría en Ciencias en Sistemas Digitales. Instituto Politécnico Nacional]. https://tesis.ipn.mx/bitstream/handle/123456789/26210/Trejo%20Z%c3%ba%c3%b1iga%2c%20Elmer%20C%c3%a9sar_DP.pdf?sequence=1&isAllowed=y

Utkin, V., Guldner, J., & Shi, J. (2017). Sliding mode control in electro-mechanical systems. CRC press.

Vijay, M., & Jena, D. (2017). PSO based neuro fuzzy sliding mode control for a robot manipulator. Journal of Electrical Systems and Information Technology, 4(1), 243-256. https://doi.org/10.1016/j.jesit.2016.08.006

Yu, W., Poznyak, A. S., & Sanchez, E. N. (1999). Neural adaptive Control of two-link manipulator with sliding mode compensation. In Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No. 99CH36288C) (Vol. 4, pp. 3122-3127). IEEE.

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