Indirect Neural Adaptive Control for Wheeled Mobile Robot
Amani Ayeb1, Abderrazak Chatti2
1Amani AYEB, physical and Instrumentation, National Institute of applied sciences and Technology, Tunis, Tunisia.
2Abderrazak Chatti, physical and Instrumentation, National Institute of applied sciences and Technology, Tunis, Tunisia.
Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 2138-2145 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4692119119/2019©BEIESP | DOI: 10.35940/ijitee.A4692.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: For a precise trajectory tracking of a wheeled mobile robot, accurate control of the position along a reference trajectory is essential. Therefore, this paper proposes an indirect neural adaptive controller for a nonholonomic mobile robot based on its dynamical model. This controller takes into account the approximation error. The use of the Lyapunov stability theorem and dynamical neural networks is indeed for deriving respectively stable learning laws for control and identification of a complex nonlinear dynamics system. The global tracking error is incorporated to adjust the neural weight learning laws to ensure the robustness of the system against approximation inaccuracy. Hence, the designed intelligent controller guarantees the convergence of both tracking and identification errors to zero. Simulation results illustrate the ability of the intelligent controller to assure the asymptotic stability of the closed-loop nonlinear uncertain system.
Keywords: Wheeled Mobile Robot, Lyapunov Stability, Indirect Neural Adaptive Control, Tracking Error, Approximation Error
Scope of the Article: Autonomous Robots