Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot

Castillo, Oscar; Martínez-Marroquín, Ricardo; Melín, Patricia; Valdez, Fevrier; Soria, José Manuel Nieto

doi:10.1016/j.ins.2010.02.022

Cited by 252 publications

(130 citation statements)

References 6 publications

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“…The robots move according to the global best (g-best) position of a particle in every iteration. To prepare an optimal intelligent controller for an autonomous wheeled mobile robot, the Castillo et al [120] have designed the hybridization of an Ant Colony Optimization (ACO) algorithm and the Particle Swarm Optimization (PSO) algorithm to optimize the membership function of a fuzzy controller. Zhang et al [121] have proposed the Multi-Objective Particle Swarm Optimization Algorithm (MOPSO) to search a collision-free optimal path in the uncertain dynamic environment.…”

Section: Particle Swarm Optimization Algorithm For Mobile Robot Navigmentioning

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: Particle Swarm Optimization Algorithm For Mobile Robot Navigmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

178

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“…These optimal controllers were used for the trajectory tracking control of autonomous mobile robots. Another paper proposed by Castillo et al [29] describes the application of GA, ACO and PSO on the optimization of the MF's parameters of T1 and T2 FLS in order to find the optimal intelligent controller for an autonomous wheeled mobile robot. Results indicated that ACO outperforms PSO and GA, respectively.…”

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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“…The optimization of the MF parameters of a type-2 Mamdani FLS was presented in [24] based on fuzzy genetic architecture. Recently, the swarm optimization techniques, particle swarm optimization (PSO) and Ant Colony (AC) techniques have been proposed to automatically tune the FLC parameters and the membership function [25]- [33]. The sliding mode control theory (SMC) based on the variable structure system can be the good choice especially for the complicated environment and systems with uncertainty disturbance [34].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Sliding Mode Controller for Grid Interface Ocean Wave Energy Conversion

El-Gammal¹

2014

JILSA

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This paper presents a closed-loop vector control structure based on adaptive Fuzzy Logic Sliding Mode Controller (FL-SMC) for a grid-connected Wave Energy Conversion System (WECS) driven Self-Excited Induction Generator (SEIG). The aim of the developed control method is to automatically tune and optimize the scaling factors and the membership functions of the Fuzzy Logic Controllers (FLC) using Multi-Objective Genetic Algorithms (MOGA) and Multi-Objective Particle Swarm Optimization (MOPSO). Two Pulse Width Modulated voltage source PWM converters with a carrier-based Sinusoidal PWM modulation for both Generator-and Grid-side converters have been connected back to back between the generator terminals and utility grid via common DC link. The indirect vector control scheme is implemented to maintain balance between generated power and power supplied to the grid and maintain the terminal voltage of the generator and the DC bus voltage constant for variable rotor speed and load. Simulation study has been carried out using the MATLAB/Simulink environment to verify the robustness of the power electronics converters and the effectiveness of proposed control method under steady state and transient conditions and also machine parameters mismatches. The proposed control scheme has improved the voltage regulation and the transient performance of the wave energy scheme over a wide range of operating conditions.

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Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot

Cited by 252 publications

References 6 publications

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

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

Adaptive Fuzzy Sliding Mode Controller for Grid Interface Ocean Wave Energy Conversion

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