Optimization of Fuzzy Logic Controllers with Distributed Bio-Inspired Algorithms

Mancilla, Alejandra; Castillo, Oscar; Valdez, Mario García

doi:10.1007/978-3-030-58728-4_1

Cited by 9 publications

(12 citation statements)

References 24 publications

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“…We must define the structure of the fuzzy controller consisting of fuzzy rules and parameterizable membership functions. Extending our previous work [43], in which we used three MFs for each variable, we now add two more membership functions to the previous fuzzy model, intending to have a finer grain of control. Adding more MFs also adds more complexity to the rules, and now we have even more parameters to tune.…”

Section: Parameterizable Fuzzy Controllermentioning

confidence: 99%

“…Usually, we keep all the parameters in the same range when using a population-based metaheuristic. In our previous work [43], we compared two ranges, [0, 1] and [0, 2]. Our experiments showed better results with the narrower range, so we selected the same configuration for the three MFs controllers for this work.…”

Section: Parameterizable Membership Functionsmentioning

confidence: 99%

“…In our previous work [43] we used a GA to evolve the controllers using a single path; we noticed that this could lead to over-training. Similar to what happens in supervised learning algorithms, the controller found by the GA could be specialized only to the path used for its evolution.…”

Section: Metaheuristic Optimization Proceduresmentioning

confidence: 99%

“…To achieve that, we first designed the structure of a parameterizable fuzzy controller, using five membership functions for each fuzzy input variable. In our previous work [42,43], we compared symmetrical and asymmetrical definitions and found that symmetrical restrictions give better results for rear-wheel-based controllers. We propose a new parameterization technique that enables symmetric MFs' definition by using an aperture factor instead of the previous method that used a delta from a fixed point.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based Metaheuristics

et al. 2022

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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.

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Section: Parameterizable Fuzzy Controllermentioning

confidence: 99%

Section: Parameterizable Membership Functionsmentioning

confidence: 99%

Section: Metaheuristic Optimization Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based Metaheuristics

et al. 2022

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“…In previous work [14], we established that tuning the MFs of fuzzy controllers using population-based metaheuristics demands the extensive use of computational resources. This demand stems from establishing the fitness of all candidate solutions, which requires running several simulations [15,16] for each candidate; this is a problem inherent to population-based algorithms.…”

Section: Introductionmentioning

confidence: 99%

Distributed and Asynchronous Population-Based Optimization Applied to the Optimal Design of Fuzzy Controllers

et al. 2023

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Designing a controller is typically an iterative process during which engineers must assess the performance of a design through time-consuming simulations; this becomes even more burdensome when using a population-based metaheuristic that evaluates every member of the population. Distributed algorithms can mitigate this issue, but these come with their own challenges. This is why, in this work, we propose a distributed and asynchronous bio-inspired algorithm to execute the simulations in parallel, using a multi-population multi-algorithmic approach. Following a cloud-native pattern, isolated populations interact asynchronously using a distributed message queue, which avoids idle cycles when waiting for other nodes to synchronize. The proposed algorithm can mix different metaheuristics, one for each population, first because it is possible and second because it can help keep total diversity high. To validate the speedup benefit of our proposal, we optimize the membership functions of a fuzzy controller for the trajectory tracking of a mobile autonomous robot using distributed versions of genetic algorithms, particle swarm optimization, and a mixed-metaheuristic configuration. We compare sequential versus distributed implementations and demonstrate the benefits of mixing the populations with distinct metaheuristics. We also propose a simple migration strategy that delivers satisfactory results. Moreover, we compare homogeneous and heterogenous configurations for the populations’ parameters. The results show that even when we use random heterogeneous parameter configuration in the distributed populations, we obtain an error similar to that in other work while significantly reducing the execution time.

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Evolutionary Approach to the Optimal Design of Fuzzy Controllers for Trajectory Tracking

Mancilla

Castillo

García-Valdéz

2021

Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation

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Optimization of Fuzzy Logic Controllers with Distributed Bio-Inspired Algorithms

Cited by 9 publications

References 24 publications

Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based Metaheuristics

Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based Metaheuristics

Distributed and Asynchronous Population-Based Optimization Applied to the Optimal Design of Fuzzy Controllers

Evolutionary Approach to the Optimal Design of Fuzzy Controllers for Trajectory Tracking

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