Design of a Backstepping-Controlled Boost Converter for MPPT in PV Chains

Boutebba, Okba; Semcheddine, Samia; Krim, Fateh; Talbi, Billel

doi:10.1109/icaee47123.2019.9014748

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…They are better suited than traditional techniques for improving reaction period, tracking efficiency, and minimizing overshoot in the period of transition, as well as oscillation associated with the MPP due to irradiation and/or temperature fluctuations [10]. (FL) is currently utilized for monitoring the MPP of solar power plants since it is resilient, relatively straightforward to develop, and requires no expertise in a specific model [11]. In this article, theoretical representations of solar energy systems and DC-DC conversion devices are utilized to analyze the FL-based MPPT approach.…”

Section: Introductionmentioning

confidence: 99%

A Study on An MPPT Control Approach Using Artificial Intelligence and the Perturb and Observe Method

Talal Bahar,

G. Rashid

2024

DJES

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A maximum power point tracking (MPPT) control procedure constructed based on the Artificial intelligence optimization algorithm is proposed. A mathematical model of photovoltaic cells was established under varying light intensity and ambient temperature. Maximal power tracking control for iterative search using an artificial intelligence (AI) optimization technique Excellent, the artificial intelligence-based MPPT algorithm's principle is presented. Simulation results it shows that the artificial intelligence algorithm can faster and exactly track the maximum power point and remains stable, and compared to Perturb and Observe algorithm under dynamic shadow conditions and artificial intelligence MPPT control method, which has higher tracking accuracy and faster convergence speed. Faster and smaller oscillation amplitude, which is the best response to sunlight conditions for photovoltaic maximum power point tracking technology Flexibility. Using Matlab/Simulink software to build a simulation model of an independent photovoltaic system. It controls variables by keeping the temperature continuous and changes the light intensity to simulate different lighting environments. The identical comparison was conducted between all based MPPT methods such as the fuzzy control approach, demonstrating that the fuzzy control based technique exhibits higher results in terms of efficiency.

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Section: Introductionmentioning

confidence: 99%

A Study on An MPPT Control Approach Using Artificial Intelligence and the Perturb and Observe Method

Talal Bahar,

G. Rashid

2024

DJES

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“…This inaccuracy can lead to unstable operation of the power converter once the control loop is closed. As a solution to instability problems and ineffective performance, several robust non-linear controllers have been proposed, such as predictive control [3], [4], intelligent control [5], [6], Sliding Mode Control (SMC) [7]- [9], and Back-Stepping (BS) [10]- [13]. These controls provide a fast transient response time and robustness.…”

Section: Introductionmentioning

confidence: 99%

Robust Non-Linear Controller Design for DC-DC Buck Converter via Modified Back-Stepping Methodology

Boutebba

Semcheddine

Krim

et al. 2022

ELEKTRON ELEKTROTECH

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This paper introduces two improved control algorithms for DC-DC converters. The first one is called “Non-Adaptive Modified Back-Stepping Control” (M-BSC) and the second one is called “Adaptive Modified Back-Stepping Control” (AM-BSC). Both the proposed control schemes allow one to increase the robustness to load and input voltage variations and make the DC-DC converter less sensitive to disturbances concerning the control algorithms available in the literature. The control aims to keep the output voltage at the desired value despite any changes that may occur during its operation. As a case study, the proposed control techniques have been applied to a DC-DC Buck converter. To validate the theoretical results and evaluate the performance of the proposed control algorithms, numerical simulations with four different scenarios have been analyzed: nominal operating conditions, load variations, output voltage tracking, and input voltage variations. The simulation results highlight the good performance of the proposed control algorithms compared to other classical algorithms, improving both the stationary error and the response time.

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Nonlinear Adaptive Neural Control of Power Converter‐Driven DC Motor System: Design and Experimental Validation

Nizami,

Gangula,

Udumula

et al. 2024

Engineering Reports

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This article presents an intelligent adaptive neural control scheme to track the output speed trajectory in power converter‐driven DC motor system. The proposed technique integrates an adaptive polynomial‐neural network with a backstepping strategy to yield a robust control system for output tracking in DC motor. Such a unification of online neural network‐based estimation and adaptive control, results in effective regulation of the output across a wide load torque uncertainties, besides yielding a promising transient and steady‐state performance. The stability of the entire closed‐loop system is ensured through Lyapunov stability criterion. The efficacy of the proposed strategy is revealed through an extensive experimental investigation under various operating points during start‐up, step‐reference tracking, and external step‐load torque disturbances. The real‐time experimentation is conducted on a laboratory prototype of power converter‐driven DC motor of 200 W, using dspace DS1104 control board with MPC8240 processor. The results obtained confirm an improvement in the transient response of the output speed by significantly reducing the settling time to and yielding a steady state behavior with no peak over/undershoots during load disturbances, in contrast to other similar works presented in the literature intended for same the application.

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Design of a Backstepping-Controlled Boost Converter for MPPT in PV Chains

Cited by 7 publications

References 16 publications

A Study on An MPPT Control Approach Using Artificial Intelligence and the Perturb and Observe Method

A Study on An MPPT Control Approach Using Artificial Intelligence and the Perturb and Observe Method

Robust Non-Linear Controller Design for DC-DC Buck Converter via Modified Back-Stepping Methodology

Nonlinear Adaptive Neural Control of Power Converter‐Driven DC Motor System: Design and Experimental Validation

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