Indonesian Journal of Electrical Engineering and Computer Science Vol. 18, No. 1, April 2020, pp. 326~334 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v18.i1.pp326-334  326 Journal homepage: http://ijeecs.iaescore.com Type-2 fuzzy logic controller optimized by wavelet networks for mobile robot navigation Fatma Affane, Kadda Zemalache Meguenni, Abdelhafid Omari LDEE, Université des Sciences et de la Technologie d’Oran-Mohamed Boudiaf, Algeria Article Info ABSTRACT Article history: Received Mar 17, 2019 Revised Jul 7, 2019 Accepted Sep 2, 2019 In this work, we will use a new control strategy based on the integration of a type-2 fuzzy reasoning optimized by wavelet networks as part of a navigation system of a mobile robot. The proposed approach is able to facilitate the navigation task in an autonomous manner, in order to determine which commands must be sent at each moment to the mobile robot. This operation must take into account convergence towards a goal with the shortest possible path in the minimum delay between the starting position and the target position. Once the goal is reached, the robot stops. Keywords: Mobile robot Navigation Optimization Type- 2 fuzzy logic controller Wavelets Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved. Corresponding Author: Fatma Affane, Department d’Automatique Université des Sciences et de la Technologies d’Oran (USTO-MB), Algerie. Email: fatma.affane@univ-usto.dz 1. INTRODUCTION The theory of fuzzy logic has been established by L.zadeh [1]. This logic allows the representation and processing of inaccurate or approximate knowledge. It is expressed by a set of linguistic rules called fuzzy rules, which are used to control complex systems or scarcely modeled [2-4]. The number of applications based on fuzzy logic theory in the field of mobile robotics has increased significantly in recent years [5-12]. Since fuzzy systems are built from the knowledge provided by the human expert, they are tainted with uncertainties. These uncertainties are injected into the membership functions of the fuzzy antecedent and consequent sets that will be uncertain. These fuzzy systems, called fuzzy type-1 systems, are incapable of modeling these uncertainties because they use specific membership functions, which have a two-dimensional representation. Therefore, fuzzy type-2 systems, whose membership functions themselves are unclear, are the extension of type-1 fuzzy systems. In recent years, several works have been developed based on type-2 fuzzy systems. They are used inimage processing [13-15], the control of electrical machines [16-19], and the control of mobile robots [20-23]. But the disadvantage of fuzzy logic is the empirical choice of parameters, which can make the control of the system long and delicate in certain situations. This problem prompted researchers to propose methods for the automatic optimization of certain parameters of the fuzzy controller, we can quote the work of [24] who designed an evolutionary algorithm to optimize the type 2 fuzzy controller and used it for tracking control of autonomous mobile robots trajectory, there is also the work of [25] who proposed a new method namely the uncontrolled genetic sorting algorithm for optimizing a proportional-integral-derivative type-2fuzzy logic controller for the follow-up control of trajectory of a Delta parallel robot. In the work [26], the authors controlled the cooperation of the robots and the tasks of reaching the target when navigating for several mobile robots using a type-2 fuzzy logic controller optimized by the PSO method. Our goal is to optimize type-2 fuzzy logic controller formobile