Metaheuristic Optimization Techniques for an Electromagnetic Multilayer Radome Design

Nguyen, Trung Kiên; Lee, In-Gon; Kwon, Obum; Kim, Yoon-Jae; Hong, Ic‐Pyo

doi:10.26866/jees.2019.19.1.31

Cited by 9 publications

(5 citation statements)

References 5 publications

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“…A well-known control problem that is applied in colleges all over the International Journal of Intelligent Engineering and Systems, Vol. 16 world is the inverted pendulum system. Due to its strong non-linearities and lack of stability, it is a good approach to evaluating prototype controllers.…”

Section: The Dynamic Model Of the Nonlinear Inverted Pendulum Systemmentioning

confidence: 99%

“…The size of the structure is (𝑛×𝑘), which is where solutions are kept. (𝑛) is the dimensionality of the problem, (𝑘) is the dimensionality of the solution [16].…”

Section: Pheromone Representationmentioning

confidence: 99%

“…According to comparison data ISMC-WOA performs better than other approaches in terms of International Journal of Intelligent Engineering and Systems, Vol. 16 settling time and overshoot [5]. The primary goal of this effort is to create an efficient control system for an inverted pendulum system that can be used as both a mathematical model and a real-world prototype.…”

Section: Introductionmentioning

confidence: 99%

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PID Neural Controller Design for Nonlinear Inverted Pendulum System

2023

IJIES

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The inverted pendulum (IP) system, is a highly coupled, complex, nonlinear system in which the performance of the system is adversely affected by parameter uncertainty and outside disturbances. Therefore, these complications must be managed by the controllers created for such systems. The primary objective of this work is to develop four control structures, including integer order proportional integral derivative neural network controllers for inverted pendulums that deal with trajectory tracking issues. Proportional-integral-derivative neural network structure1 (PIDNNS1), proportional-integral-derivative neural network structure2 (PIDNNS2), proportional-integral-derivative neural network structure3 (PIDNNS3), and proportional-integral-derivative neural network structure4 (PIDNNS4) are the controller structures for inverted pendulum (IP) system . The ant colony optimization (ACO) is a metaheuristic optimization method that is offered to optimize. the controllers' settings while minimizing the cost function. The proposed controllers' resilience to outside disturbances and parameter uncertainty is also tested. The results using MATLAB code demonstrate that the PIDNNS4 controller, which best has a reduced cost function equal to (1.177494), (1.273627), (1.209761) for trajectory tracking, parameters uncertainty, and disturbances rejection for the inverted pendulum (IP) system. and the best controller for stabilization with a low-cost function is the PIDNNS1 controller (1.280839).

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Section: The Dynamic Model Of the Nonlinear Inverted Pendulum Systemmentioning

confidence: 99%

“…The size of the structure is (𝑛×𝑘), which is where solutions are kept. (𝑛) is the dimensionality of the problem, (𝑘) is the dimensionality of the solution [16].…”

Section: Pheromone Representationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PID Neural Controller Design for Nonlinear Inverted Pendulum System

2023

IJIES

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“…In accordance with Equations (20) and 21, a material database containing several variants of Si 3 N 4 with different porosities has been created, as shown in Table 1.…”

Section: Preparation Of Silicon Nitride Based Materials Databasementioning

confidence: 99%

“…On an identical note, the optimization of thickness as well as permittivity profile of a C-sandwich radome has been attempted using PSO in [19]. Further, Nguyen et al [20] used PSO as well as ant colony optimization algorithm for obtaining the thickness profile of a C-sandwich radome. However, the application of PSO for the simultaneous optimization of material layer sequence and thickness profile of multi-layered radomes is not reported in literature to the best of our knowledge.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design Methodology for Planar Multi-Layered Radomes for Multiband Applications Using Nature Inspired Algorithm

Joy¹,

Teena²,

Singh³

et al. 2020

PIER C

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An efficient nature inspired algorithm based on particle swarm optimization (PSO) is presented in this paper for the optimal design of planar multi-layered radomes for multiband applications. Material layer sequence and thickness profile are the two critical factors determining the position of passbands in the frequency range of operation as well as the transmission performance in those bands. These design aspects have to be appropriately optimized to achieve the desired performance, and it becomes a daunting task for radome designers when a comparatively large database of suitable materials is available in the solution space. Even though commercially available software packages provide options (like particle swarm optimization (PSO), genetic algorithm (GA), etc.) for the optimization of thickness profile, they do not have the functionality for optimizing the position of a specific material inside the multi-layered radome wall configuration. In this regard, the proposed PSObased algorithm automatically chooses suitable materials from the predefined database and optimizes the thickness for each layer, in order to achieve superior transmission in user defined passbands. Furthermore, the superiority of the indigenously developed algorithm over the optimization techniques available in full wave simulation software (FEKO) w.r.t. accuracy and computational efficiency is also established using suitable case studies and validations. Although PSO has been used in the context of radomes, its application for the simultaneous optimization of material layer sequence and thickness profile of multi-layered radomes is not reported in literature to the best of our knowledge.

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