Optimization of Fuzzy Controller Using Galactic Swarm Optimization with Type-2 Fuzzy Dynamic Parameter Adjustment

Bernal, Emer; Castillo, Oscar; Soria, José Manuel Nieto; Valdez, Fevrier

doi:10.3390/axioms8010026

Cited by 35 publications

(11 citation statements)

References 40 publications

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“…This is intended to provide the basic concepts needed to understand the algorithm using input variables that were obtained in Section 3 and rules on which to base the value of output system determination. In its robustness for controlling nonlinear systems with variation and uncertainties, the fuzzy type-2 method has proven to be a strong tool for controlling complex systems [55][56][57][58][59]. The concept of the type-2 fuzzy set was introduced by Zadeh [60,61].…”

Section: Interval Type-2 Fuzzy Logic Systemmentioning

confidence: 99%

Visual-Servoing Based Global Path Planning Using Interval Type-2 Fuzzy Logic Control

Dirik

Castillo

Kocamaz

2019

Axioms

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Mobile robot motion planning in an unstructured, static, and dynamic environment is faced with a large amount of uncertainties. In an uncertain working area, a method should be selected to address the existing uncertainties in order to plan a collision-free path between the desired two points. In this paper, we propose a mobile robot path planning method in the visualize plane using an overhead camera based on interval type-2 fuzzy logic (IT2FIS). We deal with a visual-servoing based technique for obstacle-free path planning. It is necessary to determine the location of a mobile robot in an environment surrounding the robot. To reach the target and for avoiding obstacles efficiently under different shapes of obstacle in an environment, an IT2FIS is designed to generate a path. A simulation of the path planning technique compared with other methods is performed. We tested the algorithm within various scenarios. Experiment results showed the efficiency of the generated path using an overhead camera for a mobile robot.

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Section: Interval Type-2 Fuzzy Logic Systemmentioning

confidence: 99%

Visual-Servoing Based Global Path Planning Using Interval Type-2 Fuzzy Logic Control

Dirik

Castillo

Kocamaz

2019

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“…The type-1 fuzzy systems have previously been optimized with metaheuristic algorithms; for example, the optimization of type-1 fuzzy controllers is discussed in Lagunes et al (2019), which uses the firefly method to optimize fuzzy controllers of autonomous mobile robots (AMRs). Galactic Swarm Optimization (GSO) was also utilized to optimize a fuzzy controller for an autonomous robot following a trajectory in Bernal et al (2019), where the dynamic adjustment of the most critical parameters for the GSO algorithm's operation is described. The GSO algorithm was also employed in the optimization of the liquid-level fuzzy controller in Bernal et al (2020).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-based metaheuristic algorithm for optimization of fuzzy controller: fault-tolerant control application

Patel

2022

IJICC

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PurposeFuzzy-based metaheuristic algorithm is used to optimize the fuzzy controllers for the nonlinear level control system subject to uncertainty specially in the main actuator that has lost effectiveness (LOE). To optimize the fuzzy controller, type-1 harmonic search (HS) and interval type-2 (HS) will be used.Design/methodology/approachThe type-1 and type-2 fuzzy-based HS algorithms are designed for optimization of fuzzy controllers for Fault-Tolerant Control (FTC) applications, and this research proposes a fuzzy-based HS metaheuristic method. The performance of a fuzzy logic-based HS algorithm applied to a nonlinear two-tank level control process with a main actuator that has lost effectiveness (LOE) and also the same controller will be tested on DC motor angular position control with and without noise.FindingsThe key contribution of this work is the discovery of the best approach for generating an optimal vector of values for the fuzzy controller's membership function optimization. This is done in order to improve the controller's performance, bringing the process value of the two-tank level control process closer to the target process value (set point). It is worth noting that the type-1 fuzzy controller that has been optimized is an interval type-2 fuzzy system, which can handle more uncertainty than a type-1 fuzzy system.Originality/valueThe type-1 and type-2 fuzzy-based HS algorithms are designed for optimization of fuzzy controllers for FTC applications, and this research proposes a fuzzy-based HS metaheuristic method. The performance of a fuzzy logic-based HS algorithm applied to a nonlinear two-tank level control process with a main actuator that has LOE will be tested on DC motor angular position control with noise. Two nonlinear uncertain processes are used to demonstrate the effectiveness of the proposed control scheme.

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“…We therefore must optimize the matching sequences with a short period of time or with a low degree of complexity. It's one of the major reasons researchers are increasingly turning to alternative approaches [4,5,6] .…”

Section: Introductionmentioning

confidence: 99%

A Novel Population-based Optimization for Multiple Sequence Alignment in Protein Sequencing

Goswami¹,

Dubey²

2022

Eng. Sci.

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This paper proposes a novel Population-based optimization (PBO) approach to use in conjunction with an upgraded migration operator. It can tackle multiple sequence alignment (MSA) problems which can be implemented to solve the protein sequences problem. In the present state of development, it is a unique population-based algorithm based on genetic algorithms. Further, the lack of population variety and the sluggish convergence pace can be ascribed to the lack of population variety. This improved migration operator absorbs more information from various habitat types, maintains population variation, and maintains its ability to exploit new possibilities. The performance of the proposed approach has been compared to that of many current techniques, including VDGA, MOMSA, and GAPAM, using publicly available benchmark datasets, with the results showing that it outperforms them all (such as the Bali base). It has been observed that in most cases, the suggested approach outperforms and/or is competitive with the existing techniques.

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Optimization of Fuzzy Controller Using Galactic Swarm Optimization with Type-2 Fuzzy Dynamic Parameter Adjustment

Cited by 35 publications

References 40 publications

Visual-Servoing Based Global Path Planning Using Interval Type-2 Fuzzy Logic Control

Visual-Servoing Based Global Path Planning Using Interval Type-2 Fuzzy Logic Control

Fuzzy-based metaheuristic algorithm for optimization of fuzzy controller: fault-tolerant control application

A Novel Population-based Optimization for Multiple Sequence Alignment in Protein Sequencing

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