Regular Papers

International Journal of Control, Automation and Systems 2021; 19(12): 4067-4077

Published online December 6, 2021

https://doi.org/10.1007/s12555-020-0582-7

© The International Journal of Control, Automation, and Systems

Neural Network Based Adaptive Dynamic Surface Control for Omnidirectional Mobile Robots Tracking Control with Full-state Constraints and Input Saturation

Changshun Wang, Dan Wang*, and Yaozhen Han

Dalian Maritime University

Abstract

This paper presents an adaptive neural network dynamic surface controller for four-Macanum-wheeled omnidirectional mobile robots (MWOMRs) trajectory tracking with full state constraints and input saturation. First of all, an adaptive dynamic surface controller is proposed for the MIMO nonlinear systems with uncertainties and disturbances. The neural network is utilized to approximate the uncertain dynamics. A second-order tracking differentiator, instead of the traditional first-order filter, is introduced to overcome the problem of “explosion of complexity” in back-stepping technique and reduce the filtering error. By employing a barrier Lyapunov function and an auxiliary compensator based on Nussbaum function, the full state constraints and input saturation of the MWOMRs are not violated. Moreover, it is proved that all the signals in the closed-loop system with suitable parameters are semi-global uniformly bounded and the tracking error converges to an arbitrarily small compact set to zero. Finally, experiment results are presented to verify the effectiveness and robustness of the proposed adaptive control approach.

Keywords Barrier Lyapunov function, full-state constraints, input saturation, omnidirectional mobile robot, tracking control

Article

Regular Papers

International Journal of Control, Automation and Systems 2021; 19(12): 4067-4077

Published online December 1, 2021 https://doi.org/10.1007/s12555-020-0582-7

Copyright © The International Journal of Control, Automation, and Systems.

Neural Network Based Adaptive Dynamic Surface Control for Omnidirectional Mobile Robots Tracking Control with Full-state Constraints and Input Saturation

Changshun Wang, Dan Wang*, and Yaozhen Han

Dalian Maritime University

Abstract

This paper presents an adaptive neural network dynamic surface controller for four-Macanum-wheeled omnidirectional mobile robots (MWOMRs) trajectory tracking with full state constraints and input saturation. First of all, an adaptive dynamic surface controller is proposed for the MIMO nonlinear systems with uncertainties and disturbances. The neural network is utilized to approximate the uncertain dynamics. A second-order tracking differentiator, instead of the traditional first-order filter, is introduced to overcome the problem of “explosion of complexity” in back-stepping technique and reduce the filtering error. By employing a barrier Lyapunov function and an auxiliary compensator based on Nussbaum function, the full state constraints and input saturation of the MWOMRs are not violated. Moreover, it is proved that all the signals in the closed-loop system with suitable parameters are semi-global uniformly bounded and the tracking error converges to an arbitrarily small compact set to zero. Finally, experiment results are presented to verify the effectiveness and robustness of the proposed adaptive control approach.

Keywords: Barrier Lyapunov function, full-state constraints, input saturation, omnidirectional mobile robot, tracking control

IJCAS
May 2024

Vol. 22, No. 5, pp. 1461~1759

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