Modified Elman Neural-PID Controller Design for DC-DC Buck Converter System Based on Dolphin Echolocation Optimization

Dagher, Khulood E.

doi:10.22153/kej.2018.01.009

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…To keep the simplicity of these controllers and improve their dynamics, adaptive mechanism have been used for PID-Based techniques for higher efficiency. Some of the main techniques used to optimize the PID-based controllers are as follows: Sliding-Mode PID scheme [10,11], Fuzzy-PID technique [12][13][14] , Fractional-order PID controllers [15,16] , and Neural PID techniques [17,18]. All of these methods exhibit improved control, greater robustness against disruptions and uncertainty, and better acclimatisation to the unpleasant conditions.…”

Section: Introductionmentioning

confidence: 99%

Lyapunov-Based PID controller design for Buck converter with Real-time Implementation

khandan,

Delshad,

Jafari

et al. 2023

Preprint

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This paper presents a Lyapunov-based model reference controller for Buck converter operating under harmful disturbances. This strategy is an advanced version of the tuning method utilising Lyapunov stability function to reach a higher stability and a better disturbance rejection behavior in the practical applications. In addition, to reduce the computational burden and increase ease of implantation, Black-box technique is considered assuming no accurate mathematical model for the system. Lyapunov stability theory is used to enhance and tune the PID gain of this method with an adaptive mechanism while taking into account the real-time condition of a converter with regular changes. For this gain-based controller, an adaptive mechanism based on the Lyapunov concept is proposed, which can improve the stability and resilience of the system under various disturbances, particularly noise. Moreover, a Fuzzy-based PID controller and a PID technique with Particle swarm Optimization (PSO) algorithm are designed and compared with the presented Lyapunov-based technique. MATLAB\Simulink is utilized to examine the results of this improved controller. Additionally, this method produces better results in real-time contexts with faster dynamics and better frequency adaptation.

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Section: Introductionmentioning

confidence: 99%

Lyapunov-Based PID controller design for Buck converter with Real-time Implementation

khandan,

Delshad,

Jafari

et al. 2023

Preprint

View full text Add to dashboard Cite

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A smart parametrisation for robust adaptive PI controller applied on renewable energy power generation systems under weak and uttermost weak grid conditions

de Oliveira Evald,

Hollweg,

Borin

et al. 2024

Computers and Electrical Engineering

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Adaptive neuro‐fuzzy inference systems controller design on Buck converter

Nejad,

Ghamari,

Mollaee

2023

The Journal of Engineering

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Adaptive neuro‐fuzzy inference system (ANFIS) approach is designed for a Buck converter. Because DC–DC converters are under the negative impact of different disturbances, a need for a well‐behaved technique is felt to provide higher robustness in various scenarios, including parametric variations, load uncertainty, supply voltage variation, and noise. Therefore, the fuzzy logic‐based controller is adopted for this structure that provides better error detection and correction, more comprehensive range of operating conditions, and is more readily customizable. However, the fuzzy technique suffers from slow dynamics, lack of reliability against broader range of disturbances, and has a huge computational burden. To overcome the weaknesses addressed before, this technique combined with an artificial neural network (ANN) system that can tune the fuzzy part resulting in an adaptive and robust structure. ANFIS method is a promising approach that has two soft‐computing control structures, including a fuzzy logic‐based part consisting of ANN. This combination has provided many significant benefits over fuzzy logic, such as low computational burden with faster dynamics, higher flexibility with adaptable rules, and a simple structure providing ease of practical implantation; also, it does not need a mathematical moulding of the system since the whole system has been considered as a Black‐box system. To better show the superiority of this method, two other control schemes are designed as fuzzy‐based PID technique and PID controller optimized by PSO algorithm. Finally, the ANFIS control strategy is tested in various working cases through simulation and experiment results as a beneficial alternative for practical applications.

show abstract

Modified Elman Neural-PID Controller Design for DC-DC Buck Converter System Based on Dolphin Echolocation Optimization

Cited by 4 publications

References 7 publications

Lyapunov-Based PID controller design for Buck converter with Real-time Implementation

Lyapunov-Based PID controller design for Buck converter with Real-time Implementation

A smart parametrisation for robust adaptive PI controller applied on renewable energy power generation systems under weak and uttermost weak grid conditions

Adaptive neuro‐fuzzy inference systems controller design on Buck converter

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