Design of a Fuzzy Optimization Control Structure for Nonlinear Systems: A Disturbance-Rejection Method

Charfeddine, Samia; Boudjemline, Attia; Aoun, Sondess Ben; Jerbi, Houssem; Kchaou, Mourad; Alshammari, Obaid; Elleuch, Zied; Abbassi, Rabeh

doi:10.3390/app11062612

Cited by 10 publications

(7 citation statements)

References 48 publications

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“…Compared to other types of generators, it produces less noise. At the same time, being small and lightweight, DFIGs [40] are also based on WECS and are a suitable solution for them.…”

Section: The Paper Contributionmentioning

confidence: 99%

“…Many works have merged SMC to remove the chattering problem, such as artificial intelligence [38], adaptive control technique, sliding mode predictive control [39], and reach law SMC [40]. The main advantages of these techniques are their independence from specific prior knowledge of the system dynamics and their ability to suppress errors caused by parameter variation.…”

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confidence: 99%

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Robust Adaptive Super Twisting Algorithm Sliding Mode Control of a Wind System Based on the PMSG Generator

et al. 2023

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In the field of optimizing wind system control approaches and enhancing the quality of electricity generated on the grid, this research makes a fresh addition. The Sliding Mode Control (SMC) technique produces some fairly intriguing outcomes, but it has a severe flaw in the oscillations (phenomenon of reluctance: chattering) that diminish the system’s efficiency. In this paper, an AST (adaptive super twisting) approach is proposed to control the wind energy conversion system of the permanent magnet synchronous generator (PMSG), which is connected to the electrical system via two converters (grid-side and machine-side) and a capacitor serves as a DC link between them. This research seeks to regulate the generator and grid-side converter to monitor the wind rate reference given by the MPPT technique in order to eliminate the occurrence of the chattering phenomenon. With the help of this approach, precision and stability flaws will be resolved, and the wind system will perform significantly better in terms of productivity. To evaluate the performance of each control in terms of reference tracking, response time, stability, and the quality of the signal sent to the network under different wind conditions, a detailed description of the individual controls is given, preceded by a simulation in the Matlab/Simulink environment. The simulation study validates the control method and demonstrates that the AST control based on the Lyapunov stability theory provides excellent THD and power factor results. This work is completed by a comparative analysis of the other commands to identify the effect on the PMSG wind energy conversion system.

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“…Compared to other types of generators, it produces less noise. At the same time, being small and lightweight, DFIGs [40] are also based on WECS and are a suitable solution for them.…”

Section: The Paper Contributionmentioning

confidence: 99%

mentioning

confidence: 99%

Robust Adaptive Super Twisting Algorithm Sliding Mode Control of a Wind System Based on the PMSG Generator

et al. 2023

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“…Another FDI approach was developed combining multidirectional PCA and wavelets to demonstrate the value of multiscale analysis by improving defect detection ladders from mono techniques [34]. The advantage of the time-scale analysis has been shown in [35][36][37] for detecting defects affecting gears. In addition, the interest of using wavelets to increase monitoring performance is shown in several works.…”

Section: Introduction and Bibliographical Reviewmentioning

confidence: 99%

Multiscale Monitoring Using Machine Learning Methods: New Methodology and an Industrial Application to a Photovoltaic System

et al. 2022

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In this study, a multiscale monitoring method for nonlinear processes was developed. We introduced a machine learning tool for fault detection and isolation based on the kernel principal component analysis (PCA) and discrete wavelet transform. The principle of our proposal involved decomposing multivariate data into wavelet coefficients by employing the discrete wavelet transform. Then, the kernel PCA was applied on every matrix of coefficients to detect defects. Only those scales that manifest overruns of the squared prediction errors in control limits were considered in the data reconstruction phase. Thus, the kernel PCA was approached on the reconstructed matrix for detecting defects and isolation. This approach exploits the kernel PCA performance for nonlinear process monitoring in combination with multiscale analysis when processing time-frequency scales. The proposed method was validated on a photovoltaic system related to a complex industrial process. A data matrix was determined from the variables that characterize this process corresponding to motor current, angular speed, convertor output voltage, and power voltage system output. We tested the developed methodology on 1000 observations of photovoltaic variables. A comparison with monitoring methods based on neural PCA was established, proving the efficiency of the developed methodology.

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“…This controller ensures a zero static error, guarantees a fast response, and obtains a stable, robust system [10][11][12][13]. Its main fields of application are robotics [14][15][16] and control of electric machines [17][18][19][20]. The SMC is considered one of the best ordering approaches for nonlinear systems due to its multiple advantages.…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Optimization Algorithm to Enhance Controllers of Photovoltaic Systems

et al. 2022

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Increasing energy needs, pollution of nature, and eventual depletion of resources have prompted humanity to obtain new technologies and produce energy using clean sources and renewables. In this paper, we design an advanced method to improve the performance of a sliding mode controller combined with control theory for a photovoltaic system. Specifically, we decouple the controlled output of the system from any perturbation source and assess the effectiveness of the results in terms of solution quality, closed-loop control stability, and dynamical convergence of the state variables. This study focuses on the climatic conditions that may affect the behavior of a solar energy plant to supply a motor with the highest possible efficiency and nominal operating conditions. The designed method enables us to obtain an optimal performance by means of advanced control techniques and a slime mould stochastic optimization algorithm. The efficiency and performance of this method are examined based on a benchmark model of a photovoltaic system via numerical analysis and simulation.

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Design of a Fuzzy Optimization Control Structure for Nonlinear Systems: A Disturbance-Rejection Method

Cited by 10 publications

References 48 publications

Robust Adaptive Super Twisting Algorithm Sliding Mode Control of a Wind System Based on the PMSG Generator

Robust Adaptive Super Twisting Algorithm Sliding Mode Control of a Wind System Based on the PMSG Generator

Multiscale Monitoring Using Machine Learning Methods: New Methodology and an Industrial Application to a Photovoltaic System

A Stochastic Optimization Algorithm to Enhance Controllers of Photovoltaic Systems

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