Metaheuristics-Based Optimization of a Robust GAPID Adaptive Control Applied to a DC Motor-Driven Rotating Beam with Variable Load

Borges, Fábio Galvão; Guerreiro, Márcio; Monteiro, Paulo Eduardo Sampaio Sampaio; Janzen, Frederic Conrad; Corrêa, Fernanda Cristina; Stevan, Sérgio Luiz; Siqueira, Hugo Valadares; Kaster, Mauricio dos Santos

doi:10.3390/s22166094

Cited by 3 publications

(2 citation statements)

References 45 publications

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“…For the application of this control technique, the discrete transfer function of the output voltage is required [18,19]. This is obtained through the state-space linear model.…”

Section: Discrete Pid Control Designmentioning

confidence: 99%

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Design of a Takagi–Sugeno Fuzzy Exact Modeling of a Buck–Boost Converter

Gotz,

Bigai,

Harteman

et al. 2023

Designs

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DC–DC converters are used in many power electronics applications, such as switching power supply design, photovoltaic, power management systems, and electric and hybrid vehicles. Traditionally, DC–DC converters are linearly modeled using a typical operating point for their control design. Some recent works use nonlinear models for DC–DC converters, due to the inherent nonlinearity of the switching process. In this sense, a standout modeling technique is the Takagi–Sugeno fuzzy exact method due to its ability to represent nonlinear systems over the entire operating range. It is more faithful to system behavior modeling, and allows a nonlinear closed-loop control design. The use of nonlinear models allows the testing of controllers obtained by linear methods to operate outside their linearization point, corroborating with robust controllers for specific applications. This work aims to perform the exact fuzzy Takagi–Sugeno modeling of a buck–boost converter with non-ideal components, and to design a discrete proportional–integral–derivative (PID) controller from the pole cancellation technique, obtained linearly, to test the controller at different operating points. The PID control ensured a satisfactory result compared with the stationary value of the different operating points, but it did not reach the desired transient response. Since the proposed model closely represents the operation of the buck–boost converter by considering the components’ non-idealities, other control techniques that consider the system’s nonlinearities can be applied and optimized later.

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“…For the application of this control technique, the discrete transfer function of the output voltage is required [18,19]. This is obtained through the state-space linear model.…”

Section: Discrete Pid Control Designmentioning

confidence: 99%

“…In addition, some adaptive PID techniques can be used to improve the performance of traditional PID controllers. Borges et al [18] and Itaborahy Filho et al [19] used metaheuristic-based optimization to design some robust controllers.…”

Section: Introductionmentioning

confidence: 99%

Design of a Takagi–Sugeno Fuzzy Exact Modeling of a Buck–Boost Converter

Gotz,

Bigai,

Harteman

et al. 2023

Designs

Self Cite

View full text Add to dashboard Cite

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Metaheuristic Approaches to Tune PID Controller for Ball on Plate System

Ekole,

Abdalla,

Shalabi

et al. 2024

Springer Proceedings in Mathematics &Amp; Statistics

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Bio-Inspired Optimization Algorithms Applied to the GAPID Control of a Buck Converter

et al. 2022

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Although the proportional integral derivative (PID) is a well-known control technique applied to many applications, it has performance limitations compared to nonlinear controllers. GAPID (Gaussian Adaptive PID) is a non-linear adaptive control technique that achieves considerably better performance by using optimization techniques to determine its nine parameters instead of deterministic methods. GAPID represents a multimodal problem, which opens up the possibility of having several distinct near-optimal solutions, which is a complex task to solve. The objective of this article is to examine the behavior of many optimization algorithms in solving this problem. Then, 10 variations of bio-inspired metaheuristic strategies based on Genetic Algorithms (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO) are selected to optimize the GAPID control of a Buck DC–DC converter. The computational results reveal that, in general, the variants implemented for PSO and DE presented the highest fitness, ranging from 0.9936 to 0.9947 on average, according to statistical analysis provided by Shapiro–Wilks, Kruskall–Wallis and Dunn–Sidak post-hoc tests, considering 95% of confidence level.

show abstract

Metaheuristics-Based Optimization of a Robust GAPID Adaptive Control Applied to a DC Motor-Driven Rotating Beam with Variable Load

Cited by 3 publications

References 45 publications

Design of a Takagi–Sugeno Fuzzy Exact Modeling of a Buck–Boost Converter

Design of a Takagi–Sugeno Fuzzy Exact Modeling of a Buck–Boost Converter

Metaheuristic Approaches to Tune PID Controller for Ball on Plate System

Bio-Inspired Optimization Algorithms Applied to the GAPID Control of a Buck Converter

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