Optimization of interval type-2 fuzzy logic controllers for a perturbed autonomous wheeled mobile robot using genetic algorithms

Martínez, Ricardo; Castillo, Oscar; Aguilar, Luis T.

doi:10.1016/j.ins.2008.12.028

Cited by 313 publications

(70 citation statements)

References 33 publications

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“…Wheeled mobile robots [21] have been widely used in various industrial applications, transportation, and social sectors, etc. Martinez et al [22] have designed the kinematics and dynamics trajectory tracking control of the autonomous unicycle mobile robot using type-2 fuzzy logic and genetic algorithms. An adaptive neural network based motion and orientation control of a nonholonomic wheeled mobile robot has been presented in [23].…”

Section: Study Of Kinematic and Dynamic Analysis Of The Wheeled Mobilmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

178

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Mobile robot is an autonomous agent capable of navigating intelligently anywhere using sensor-actuator control techniques. The applications of the autonomous mobile robot in many fields such as industry, space, defence and transportation, and other social sectors are growing day by day. The mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. Several techniques have been applied by the various researchers for mobile robot navigation and obstacle avoidance. The present article focuses on the study of the intelligent navigation techniques, which are capable of navigating a mobile robot autonomously in static as well as dynamic environments. Keywords: IntroductionThis article introduces the literature survey of the various techniques used for mobile robot navigation. Navigation and obstacle avoidance are one of the fundamental problems in mobile robotics, which are being solved by the various researchers in the past two decades. The aim of navigation is to search an optimal or suboptimal path from the start point to the goal point with obstacle avoidance competence. Basically, the mobile robot navigation has been done by the Deterministic algorithm and Nondeterministic (Stochastic) algorithm. Nowadays, the hybridization of both the algorithms called as an Evolutionary algorithm is being used to solve the mobile robot navigation problem. Figure 1 shows the general classification of the Deterministic algorithm, Nondeterministic (Stochastic) algorithm, and Evolutionary algorithm, which are implemented for mobile robot navigation by various authors.

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Section: Study Of Kinematic and Dynamic Analysis Of The Wheeled Mobilmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

178

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“…A. Zadeh in 1965, on the basis of a theory of fuzzy sets, which differs from the traditional crisp sets, because the degree of belonging is considered. At present, the use of logic systems (FLS) has increased, as can be observed in [24][25][26][27][28][29][30]. The Interval Type 2 fuzzy set has a fuzzy membership function, the membership grade for each element of this set is a fuzzy set in [0, 1], as can be observed in [31][32][33][34][35].…”

Section: Fuzzy Logic Systemsmentioning

confidence: 99%

Fuzzy Dynamic Parameter Adaptation in the Harmony Search Algorithm for the Optimization of the Ball and Beam Controller

Peraza

Valdez

Castro

et al. 2018

Advances in Operations Research

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This paper presents a method for dynamic parameter adaptation in the harmony search algorithm (HS) based on fuzzy logic. The adaptation is performed using Type 1 (FHS), interval Type 2 (IT2FHS), and generalized Type 2 (GT2FHS) fuzzy systems as the number of improvisations or iterations advances, achieving a better intensification and diversification. The main contribution of this work is the dynamic parameter adaptation using different types of fuzzy systems in the harmony search algorithm applied to optimization of the membership functions for a benchmark control problem; in this case it is focused on the ball and beam controller. Experiments are presented with the HS, FHS, IT2FHS, and GT2FHS with noise (uniform random number) and without noise for the controller, and the following error metrics are obtained: ITAE, ITSE, IAE, ISE, and RMSE, to validate the efficacy of the proposed methods.

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“…Astudillo et al [18] Logic Theory. Martinez et al [19], [20] made the same work as Astudillo but with adding GA for optimization. Leottau and Melgarejo [21] presented an approach for designing an IT2 FLS for a mobile robot application and described how it could be developed involving the use of T1 and T2 fuzzy sets.…”

Section: Related Workmentioning

confidence: 99%

An Optimized Interval Type-2 Fuzzy Logic Control Scheme based on Optimal Defuzzification

Allawi¹,

Abdalla²

2014

IJCA

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In this paper, a new way of optimizing fuzzy logic is introduced. This way is used to optimize the output of Interval Type-2 Fuzzy Logic controller by replacing the Defuzzification stage by the Optimization algorithm. The algorithm chooses the best crisp output variable from the type-reduced set which is the output of the Type-Reduction stage instead of averaging the set extremes which was performed by Defuzzification stage. Artificial Bee Colony optimization algorithm is used to optimize the Interval Type-2 Fuzzy Logic controller to manage the navigation of multiple mobile robots in indoor environments.

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Optimization of interval type-2 fuzzy logic controllers for a perturbed autonomous wheeled mobile robot using genetic algorithms

Cited by 313 publications

References 33 publications

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Fuzzy Dynamic Parameter Adaptation in the Harmony Search Algorithm for the Optimization of the Ball and Beam Controller

An Optimized Interval Type-2 Fuzzy Logic Control Scheme based on Optimal Defuzzification

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