Title: New approach for online parameter identification for non-holonomic mobile robots

Authors: Jean Marie Lauhic Ndong Mezui; Dieudonné Ekang; Donatien Nganga-Kouya; Maarouf Saad; Aime Francis Okou

Addresses: Mechanical Engineering Department and Department of Electrical Engineering, École Normale Supérieure De L'enseignement Technique, Leon Mba Boulevard, Libreville, BP 3989, Gabon ' Mechanical Engineering Department and Department of Electrical Engineering, École Normale Supérieure De L'enseignement Technique, Leon Mba Boulevard, Libreville, BP 3989, Gabon ' Mechanical Engineering Department, École Normale Supérieure de L'enseignement Technique, Leon Mba Boulevard, Libreville, BP 3989, Gabon ' Department of Electrical Engineering, École De Technologie Supérieure, 1100 Notre-Dame St W, Montreal, Quebec, H3C 1K3, Canada ' Department of Electrical and Computer Engineering, Royal Military College of Canada, 13 General Crerar Crescent, Kingston, Ontario, K7K 7B4, Canada

Abstract: The dynamic models that represent robotic systems greatly condition the strategies used for the design of their control systems. The accuracy of the model parameter values can have a significant impact on the closed-loop system performance. This paper proposes an innovative method for the online parameter identification (IOPI) of a non-holonomic mobile robot. The proposed method enables to find the inertia accurately, mass and friction parameters in the robot's model and it does not require the drive wheel input torques to be sufficiently rich signals. The identification algorithm is based on the resolution of a system of linearly independent equations. The number of equations is equal to the number of unknown parameters to be estimated. The simulation results show that with the proposed method, the estimated parameters quickly converge to the real parameters of the non-holonomic mobile robot, contrary to the recursive least squares (RLS) method.

Keywords: non-holonomic mobile robot; parameter estimation; iterative identification in continuous time of Newton; recursive least squares method.

DOI: 10.1504/IJMIC.2024.137995

International Journal of Modelling, Identification and Control, 2024 Vol.44 No.3, pp.218 - 229

Received: 07 Sep 2022
Received in revised form: 12 Jan 2023
Accepted: 24 Jan 2023
Published online: 16 Apr 2024 *

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