Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based Metaheuristics
In this work, we propose, through the use of population-based metaheuristics, an optimization method that solves the problem of autonomous path tracking using a rear-wheel fuzzy logic controller. This approach enables the design of controllers using rules that are linguistically familiar to human us...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/14/2/202 |
| _version_ | 1797476480544931840 |
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| author | Alejandra Mancilla Mario García-Valdez Oscar Castillo Juan Julian Merelo-Guervós |
| author_facet | Alejandra Mancilla Mario García-Valdez Oscar Castillo Juan Julian Merelo-Guervós |
| author_sort | Alejandra Mancilla |
| collection | DOAJ |
| description | In this work, we propose, through the use of population-based metaheuristics, an optimization method that solves the problem of autonomous path tracking using a rear-wheel fuzzy logic controller. This approach enables the design of controllers using rules that are linguistically familiar to human users. Moreover, a new technique that uses three different paths to validate the performance of each candidate configuration is presented. We extend on our previous work by adding two more membership functions to the previous fuzzy model, intending to have a finer-grained adjustment. We tuned the controller using several well-known metaheuristic methods, Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Harmony Search (HS), and the recent Aquila Optimizer (AO) and Arithmetic Optimization Algorithms. Experiments validate that, compared to published results, the proposed fuzzy controllers have better RMSE-measured performance. Nevertheless, experiments also highlight problems with the common practice of evaluating the performance of fuzzy controllers with a single problem case and performance metric, resulting in controllers that tend to be overtrained. |
| first_indexed | 2024-03-09T20:59:24Z |
| format | Article |
| id | doaj.art-7b2f8e779e6247bdba541cfa8dc2e3ca |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T20:59:24Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-7b2f8e779e6247bdba541cfa8dc2e3ca2023-11-23T22:14:56ZengMDPI AGSymmetry2073-89942022-01-0114220210.3390/sym14020202Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based MetaheuristicsAlejandra Mancilla0Mario García-Valdez1Oscar Castillo2Juan Julian Merelo-Guervós3Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana 22414, MexicoDivision of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana 22414, MexicoDivision of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana 22414, MexicoDepartment of Computer Architecture and Computer Technology, University of Granada, 16741 Granada, SpainIn this work, we propose, through the use of population-based metaheuristics, an optimization method that solves the problem of autonomous path tracking using a rear-wheel fuzzy logic controller. This approach enables the design of controllers using rules that are linguistically familiar to human users. Moreover, a new technique that uses three different paths to validate the performance of each candidate configuration is presented. We extend on our previous work by adding two more membership functions to the previous fuzzy model, intending to have a finer-grained adjustment. We tuned the controller using several well-known metaheuristic methods, Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Harmony Search (HS), and the recent Aquila Optimizer (AO) and Arithmetic Optimization Algorithms. Experiments validate that, compared to published results, the proposed fuzzy controllers have better RMSE-measured performance. Nevertheless, experiments also highlight problems with the common practice of evaluating the performance of fuzzy controllers with a single problem case and performance metric, resulting in controllers that tend to be overtrained.https://www.mdpi.com/2073-8994/14/2/202fuzzy systemsfuzzy controlbioinspired algorithms |
| spellingShingle | Alejandra Mancilla Mario García-Valdez Oscar Castillo Juan Julian Merelo-Guervós Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based Metaheuristics Symmetry fuzzy systems fuzzy control bioinspired algorithms |
| title | Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based Metaheuristics |
| title_full | Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based Metaheuristics |
| title_fullStr | Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based Metaheuristics |
| title_full_unstemmed | Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based Metaheuristics |
| title_short | Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based Metaheuristics |
| title_sort | optimal fuzzy controller design for autonomous robot path tracking using population based metaheuristics |
| topic | fuzzy systems fuzzy control bioinspired algorithms |
| url | https://www.mdpi.com/2073-8994/14/2/202 |
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