Optimal Tuning of the Speed Control for Brushless DC Motor Based on Chaotic Online Differential Evolution

Rodríguez-Molina, Alejandro; Villarreal-Cervantes, Miguel Gabriel; Serrano-Pérez, Omar; Solís-Romero, José; Silva‐Ortigoza, Ramón

doi:10.3390/math10121977

Cited by 9 publications

(4 citation statements)

References 74 publications

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“…Considering that, under the proposed experimental conditions, D-Bug0/PSO requires, on average, 96 optimization processes to complete the planning, the processing time for each process is affordable (i.e., less than dt), with an adequate budget for problem evaluations. In the case of processing equipment with fewer resources than a conventional PC, the time window to obtain a solution with the metaheuristics can be longer, as it has been observed in other online optimization works [58], however, some parameters of the proposal, such as µ, must also be readjusted. It is also interesting to observe a sketch of the behavior of D-Bug0/PSO.…”

Section: Resultsmentioning

confidence: 99%

Dynamic Path Planning for the Differential Drive Mobile Robot Based on Online Metaheuristic Optimization

et al. 2022

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Mobile robots are relevant dynamic systems in recent applications. Path planning is an essential task for these robots since it allows them to move from one location to another safely and at an affordable cost. Path planning has been studied extensively for static scenarios. However, when the scenarios are dynamic, research is limited due to the complexity and high cost of continuously re-planning the robot’s movements to ensure its safety. This paper proposes a new, simple, reliable, and affordable method to plan safe and optimized paths for differential mobile robots in dynamic scenarios. The method is based on the online re-optimization of the static parameters in the state-of-the-art deterministic path planner Bug0. Due to the complexity of the dynamic path planning problem, a metaheuristic optimization approach is adopted. This approach utilizes metaheuristics from evolutionary computation and swarm intelligence to find the Bug0 parameters when the mobile robot is approaching an obstacle. The proposal is tested in simulation, and well-known metaheuristic methods are compared, including Differential Evolution (DE), the Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The dynamic planner based on PSO generates paths with the best performances. In addition, the results of the PSO-based planner are compared with different Bug0 configurations, and the former is shown to be significantly better.

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Section: Resultsmentioning

confidence: 99%

Dynamic Path Planning for the Differential Drive Mobile Robot Based on Online Metaheuristic Optimization

et al. 2022

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“…As a last example involving the DE algorithm, we consider the work of Rodríguez-Molina et al [80], who assessed the efficiency of the controller performance in a Brushless Direct current (BLDC) motor in an uncertain environment. The performance of the BLDC depends highly on the adaptability of the controller gains.…”

Section: Optimal Tuning Of Speed Control For a Brushless DC Motormentioning

confidence: 99%

“…The exact reconstructor of system (80) for the non measured variable z k may be obtained trivially, as x is a one-step delay of z. This was readily derived to be…”

Section: Theoretical Algorithmsmentioning

confidence: 99%

Survey of Recent Applications of the Chaotic Lozi Map

Lozi

2023

Algorithms

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Since its original publication in 1978, Lozi’s chaotic map has been thoroughly explored and continues to be. Hundreds of publications have analyzed its particular structure and applied its properties in many fields (e.g., improvement of physical devices, electrical components such as memristors, cryptography, optimization, evolutionary algorithms, synchronization, control, secure communications, AI with swarm intelligence, chimeras, solitary states, etc.) through algorithms such as the COLM algorithm (Chaotic Optimization algorithm based on Lozi Map), Particle Swarm Optimization (PSO), and Differential Evolution (DE). In this article, we present a survey based on dozens of articles on the use of this map in algorithms aimed at real applications or applications exploring new directions of dynamical systems such as chimeras and solitary states.

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“…Several articles have used several types of chaotic maps for the purpose of algorithm optimization. Chaotic maps are dynamic in character and statistics built on randomness [76], [77]. Future or future behavior is affected by parameter changes.…”

Section: B the Novel Chaotic Sea-horse Optimizer (Csho)mentioning

confidence: 99%

A Novel Improved Sea-Horse Optimizer for Tuning Parameter Power System Stabilizer

Aribowo

2023

Journal of Robotics and Control

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Power system stabilizer (PSS) is applied to dampen system oscillations so that the frequency does not deviate beyond tolerance. PSS parameter tuning is increasingly difficult when dealing with complex and nonlinear systems. This paper presents a novel hybrid algorithm developed from incorporating chaotic maps into the sea-horse optimizer. The algorithm developed is called the chaotic sea-horse optimizer (CSHO). The proposed method is adopted from the metaheuristic method, namely the sea-horse optimizer (SHO). The SHO is a method that duplicates the life of a sea-horse in the ocean when it moves, looks for prey and breeds. In This paper, The CSHO method is used to tune the power system stabilizer parameters on a single machine system. The proposed method validates the benchmark function and performance on a single machine system against transient response. Several metaheuristic methods are used as a comparison to determine the effectiveness and efficiency of the proposed method. From the research, it was found that the application of the logistics Tent map from the chaotic map showed optimal performance. In addition, the application of the PSS shows effective and efficient performance in reducing overshoot in transient conditions.

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Optimal Tuning of the Speed Control for Brushless DC Motor Based on Chaotic Online Differential Evolution

Cited by 9 publications

References 74 publications

Dynamic Path Planning for the Differential Drive Mobile Robot Based on Online Metaheuristic Optimization

Dynamic Path Planning for the Differential Drive Mobile Robot Based on Online Metaheuristic Optimization

Survey of Recent Applications of the Chaotic Lozi Map

A Novel Improved Sea-Horse Optimizer for Tuning Parameter Power System Stabilizer

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