Hostname: page-component-89b8bd64d-5bvrz Total loading time: 0 Render date: 2026-05-10T00:28:31.994Z Has data issue: false hasContentIssue false
  • English
  • Français

Adaptive Neural Feedback Linearizing Control of Type (m,s) Mobile Manipulators with a Guaranteed Prescribed Performance

Published online by Cambridge University Press:  10 April 2019

Khoshnam Shojaei Open the ORCID record for Khoshnam Shojaei [Opens in a new window]  and
Ali Kazemy
Khoshnam Shojaei*
Affiliation:
Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran Digital Processing and Machine Vision Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
Ali Kazemy
Affiliation:
Department of Electrical Engineering, Tafresh University, Tafresh 39518-79611, Iran. E-mail: kazemy@tafreshu.ac.ir
*
*Corresponding author. E-mail: khoshnam.shojaee@gmail.com
Get access Rights & Permissions [Opens in a new window]

Summary

In this paper, a neural network (NN)-based tracking controller is proposed for a general class of type (m,s) wheeled mobile manipulators (WMMs) subjected to model uncertainties with prescribed transient and steady-state performance specifications. First, an input–output model of WMMs is derived by introducing proper output equations. Then, the prescribed performance technique is employed to propose a proportional integral derivative trajectory tracking controller for WMMs to ensure that the tracking errors converge to a smaller, arbitrary ultimate bound with a predefined maximum overshoot/undershoot and convergence speed. The learning capabilities of multilayer NNs are incorporated into the controller to approximate the uncertain nonlinear dynamics of the robot. An adaptive saturation-type controller is utilized to compensate NN estimation errors and external disturbances. A Lyapunov-based stability analysis is used to demonstrate that the tracking errors are uniformly ultimately bounded and converge to a small neighborhood of zero with a guaranteed prescribed performance. Numerical computer simulations are presented to show the effectiveness of the proposed controller.

Keywords

Adaptive robust controlModel uncertaintyMultilayer neural networksPrescribed performance functionTrajectory trackingWheeled mobile manipulators

Information

Type
Articles
Information
Robotica , Volume 37 , Issue 11 , November 2019 , pp. 1937 - 1955
Copyright
© Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Yamamoto, Y., Control and Coordination of Locomotion and Manipulation of Wheeled Mobile Manipulators. Ph.D. Thesis (University of Pennsylvania, 1994).Google Scholar
Dong, W., “On trajectory and force tracking control of constrained mobile manipulators with parameter uncertainty,” Automatica 38(8), 1475–1484 (2002).CrossRefGoogle Scholar
Mazur, A., “Hybrid adaptive control laws solving a path-following problem for non-holonomic mobile manipulators,” Int. J. Control 77(15), 1297–1306 (2004).CrossRefGoogle Scholar
Li, Z., Ge, S. S. and Wang, Z., “Robust adaptive control of coordinated multiple mobile manipulators,” Mechatronics 18(5–6), 239–250 (2008).CrossRefGoogle Scholar
Li, Z., Li, J. and Kang, Y., “Adaptive robust coordinated control of multiple mobile manipulators interacting with rigid environments,” Automatica 46(12), 2028–2034 (2010).CrossRefGoogle Scholar
Zhong, G., Kobayashi, Y., Hoshino, Y. and Emaru, T., “System modeling and tracking control of mobile manipulator subjected to dynamic interaction and uncertainty,” Nonlinear Dyn. 73(1), 167–182 (2013).CrossRefGoogle Scholar
Peng, J., Yu, J. and Wang, J., “Robust adaptive tracking control for nonholonomic mobile manipulator with uncertainties,” ISA Trans. 53(4), 1035–1043 (2014).CrossRefGoogle Scholar
Galicki, M., “An adaptive non-linear constraint control of mobile manipulators,” Mech. Mach. Theory 88, 63–85 (2015).CrossRefGoogle Scholar
Li, Z., Ge, S. S., Adams, M. and Vijesoma, W. S., “Adaptive robust output-feedback motion/force control of electrically driven nonholonomic mobile manipulators,” IEEE Trans. Control Syst. Technol. 16(6), 1308–1315 (2008).Google Scholar
Lin, S. and Goldenberg, A. A., “Neural-network control of mobile manipulators,” IEEE Trans. Neural Networks 12(5), 1121–1133 (2001).Google Scholar
Li, Z. and Chen, W., “Adaptive neural-fuzzy control of uncertain constrained multiple coordinated nonholonomic mobile manipulators,” Eng. Appl. Artif. Intell. 21, 985–1000 (2008).CrossRefGoogle Scholar
Xu, D., Zhao, D., Yi, J. and Tan, X., “Trajectory tracking control of omnidirectional wheeled mobile manipulators: Robust neural network-based sliding mode approach,” IEEE Trans. Syst. Man Cybern.-Part B. Cybern. 39(3), 788–799 (2009).Google Scholar
Li, Z., Yang, C. and Tang, Y., “Decentralized adaptive fuzzy control of coordinated multiple mobile manipulators interacting with non-rigid environment,” IET Control Theory Appl. 7(3), 397–410 (2013).CrossRefGoogle Scholar
Cheng, M.-B., Su, W.-C. and Tsai, C.-C., “Robust tracking control of a unicycle-type wheeled mobile manipulator using a hybrid sliding mode fuzzy neural network,” Int. J. Syst. Sci. 43(3), 408–425 (2012).CrossRefGoogle Scholar
Avanzini, G. B., Zanchettin, A. M. and Rocco, P., “Constrained model predictive control for mobile robotic manipulators,” Robotica 36(1), 19–38 (2018).CrossRefGoogle Scholar
Xiao, L., Liao, B., Li, S., Zhang, Z., Ding, L. and Jin, L., “Design and analysis of FTZNN applied to the realtime solution of a nonstationary Lyapunov equation and tracking control of a wheeled mobile manipulator,” IEEE Trans. Ind. Informatics 14(1), 98–105 (2018).CrossRefGoogle Scholar
Marino, A., “Distributed adaptive control of networked cooperative mobile manipulators,” IEEE Trans. Control Syst. Technol. 26(5), 1646–1660 (2018). doi: 10.1109/TCST.2017.2720673CrossRefGoogle Scholar
Dai, C.-B. and Liu, Y.-C., “Distributed coordination and cooperation control for networked mobile manipulators,” IEEE Trans. Ind. Electron. 64(6), 5065–5074 (2017).CrossRefGoogle Scholar
Sharma, B., Singh, S., Vanualailai, J. and Prasad, A., “Globally rigid formation of n-link doubly nonholonomic mobile manipulators,” Rob. Auton. Syst. 105, 69–84 (2018).CrossRefGoogle Scholar
Song, K.-T., Jiang, S.-Y. and Lin, M.-H., “Interactive teleoperation of a mobile manipulator using a sharedcontrol approach,” IEEE Trans. Hum.-Mach. Syst. 46(6), 834–845 (2016).CrossRefGoogle Scholar
Bechlioulis, C. and Rovithakis, G., “Robust adaptive control of feedback linearizable MIMO nonlinear systems with prescribed performance,” IEEE Trans. Autom. Control 53(9), 2090–2099 (2008).CrossRefGoogle Scholar
Wang, W., Huang, J. and Wen, C., “Prescribed performance bound-based adaptive path-following control of uncertain nonholonomic mobile robots,” Int. J. Adapt. Control Signal Process. 31, 805–822 (2017).CrossRefGoogle Scholar
Gao, T., Huang, J., Zhou, Y. and Song, Y.-D., “Robust adaptive tracking control of an underactuated ship with guaranteed transient performance,” Int. J. Syst. Sci. 48(2), 272–279 (2017).CrossRefGoogle Scholar
Karayiannidis, Y. and Doulgeri, Z., “Regressor-free prescribed performance robot tracking,” Robotica 31(8), 1229–1238 (2013).CrossRefGoogle Scholar
Li, S. and Xiang, Z., “Adaptive prescribed performance control for switched nonlinear systems with input saturation,” Int. J. Syst. Sci. 49(1), 113–123 (2018).CrossRefGoogle Scholar
Shojaei, K., “Saturated output feedback control of uncertain nonholonomic wheeled mobile robots,” Robotica 33(1), 87–105 (2015).CrossRefGoogle Scholar
Shojaei, K., “Neural adaptive output feedback control of wheeled mobile robots with saturating actuators,” Int. J. Adapt. Control Signal Proces. 29, 855–876 (2015).CrossRefGoogle Scholar
Shojaei, K., “Neural adaptive output feedback formation control of type (m, s) wheeled mobile robots,” IET Control Theory Appl. 11(4), 504–515 (2017).CrossRefGoogle Scholar
Li, Z. and Ge, S. S., Fundamentals in Modelling and Control of Mobile Manipulators (CRC Press, Boca Raton, 2013).Google Scholar
Shojaei, K., Shahri, A. M. and Tabibian, B., “Design and implementation of an inverse dynamics controller for uncertain nonholonomic robotic systems,” J. Intell. Rob. Syst. 71, 65–83 (2013).CrossRefGoogle Scholar
Shojaei, K., Tarakameh, A. and Shahri, A. M., “Adaptive Trajectory Tracking of WMRs Based on Feedback Linearization Technique,” Proceedings of the 2009 IEEE International Conference on Mechatronics and Automation, Changchun, China (2009), pp. 729–734.Google Scholar
Wang, D. and Xu, G., “Full-state tracking and internal dynamics of nonholonomic wheeled mobile robots,” IEEE ASME Trans. Mechatron. 8(2), 203–214 (2003).CrossRefGoogle Scholar
Campion, G., Bastin, G. and d’Andrea-Novel, B., “Structural properties and classification of kinematic and dyanmic models of wheeled mobile robots,” IEEE Trans. Rob. Autom. 12(1), 47–62 (1996).CrossRefGoogle Scholar
Siegwart, R., Nourbakhsh, I. R. and Scaramuzza, D., Introduction to Autonomous Mobile Robots, 2nd ed. (MIT Press, London, UK, 2011).Google Scholar
Shojaei, K. and Shahri, A. M., “Output feedback tracking control of uncertain non-holonomic wheeled mobile robots: A dynamic surface control approach,” IET Control Theory Appl. 6(2), 216–228 (2012).CrossRefGoogle Scholar
Ioannou, P. A. and Sun, J., Robust Adaptive Control (Prentice-Hall, Englewood Cliffs, NJ, 1996).Google Scholar
Lewis, F. L., Dawson, D. M. and Abdallah, C. T., Robot Manipulator Control Theory and Practice. 2nd ed. Revised and Expanded (Marcel Dekker, New York, 2004).Google Scholar
Polycarpou, M. M., “Stable adaptive neural control scheme for nonlinear systems,” IEEE Trans. Autom. Control 41(3), 447–451 (1996).CrossRefGoogle Scholar
Ge, S. S., Hang, C. C., Lee, T. H. and Zhang, T., Stable Adaptive Neural Network Control (Kluwer, Boston, MA, 2001).Google Scholar
White, G. D., Bhatt, R. M. and Krovi, V. N., “Dynamic redundancy resolution in a nonholonomic wheeled mobile manipulator,” Robotica 25(2), 147–156 (2007).CrossRefGoogle Scholar
Shojaei, K. and Shahri, A. M., “Experimental study of iterated Kalman filters for simultaneous localization and mapping of autonomous mobile robots,” J. Intell. Rob. Syst. 63(3–4), 575–594 (2011).CrossRefGoogle Scholar
Shojaei, K. and Shahri, A. M., “Iterated Unscented SLAM Algorithm for Navigation of an Autonomous Mobile Robot,” 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France (2008).CrossRefGoogle Scholar
Boukattaya, M.,Jallouli, M. and Damak, T., “On trajectory tracking control for nonholonomic mobile manipulators with dynamic uncertainties and external torque disturbances,” Rob. Auton. Syst. 60, 1640–1647 (2012).CrossRefGoogle Scholar