Mohammed Abdeldjalil Djehaf

Flowchart of the calibration workflow, from plant data and ASM1/BSM1 modelling with 19 parameters, through PRCC screening to 13 and Sobol analysis to 8, to GA and PSO calibration and model validation. This paper presents a hierarchical ASM1 calibration framework applied to the Tlemcen municipal wastewater treatment plant, Algeria, under limited data conditions, using the Benchmark Simulation Model No. 1 (BSM1) implemented in MATLAB/Simulink. This study combines Partial Rank Correlation Coefficient (PRCC) screening under steady-state conditions and Sobol variance decomposition under dynamic conditions within a sequential sensitivity analysis framework, progressively reducing the parameter space from 19 to 8 influential parameters, prior to dynamic calibration using Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), simultaneously optimizing five output variables: COD, TSS, BOD5, SNO and SNH. Based on 10 independent runs, PSO significantly outperformed GA in solution quality (mean J = 0.3150 ± 0.0001 vs 0.3167 ± 0.0009) and stability, confirmed by a Wilcoxon signed-rank test (p = 0.0020). The calibrated model showed good agreement with 2024 measured data (NRMSE <0.40, NMAE <0.41, TIC <0.22). Validation on an independent 2023 dataset confirmed temporal robustness (TIC <0.35). The proposed framework provides a practical decision support tool for aeration management and energy optimization, with potential extensions toward automatic control strategies and digital twin development for similar Algerian WWTPs operating under constrained monitoring conditions.

Traditional artificial neural networks (ANNs) in fracture mechanics often suffer from limitations such as inconsistent hyperparameter optimization, reliance on default settings without sensitivity analyses, and difficulties in ensuring generalization and reproducibility across datasets. Accurate prediction of mixed-mode fracture parameters stress intensity factors ( K I , K II ) and the T-stress is critical for structural integrity assessment. This investigation systematically evaluates ANN configurations for predicting mixed-mode fracture parameters in PMMA short beam bend specimens. It identifies the Bayesian Regularized Radial Basis Function Network (BRBFN) as a globally optimal surrogate model through an exhaustive evaluation of 6,552 unique ANN configurations, incorporating 13 training algorithms, 12 activation functions, and 42 network topologies. The BRBFN achieves exceptional accuracy on validation data ( R 2 > 0 . 997 , MSE < 0 . 033 ) with training completed in under one second. A comparative analysis against advanced deep learning architectures Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRU) demonstrates that the BRBFN significantly outperforms these complex models in generalization on unseen data (MSE improvement of 27%–35%), while requiring two orders of magnitude less training time. A six-metric evaluation framework (MSE, RMSE, MAE, IAE, ITAE, R 2 ) confirms the robustness of this configuration across complementary performance criteria. This establishes a reproducible and efficient methodology for deploying reliable ANNs in fracture mechanics, minimizing overfitting risks in limited-data regimes. Validation against experimental fracture initiation angles confirms practical applicability, with predictions closely mirroring the generalized maximum tangential stress criterion.

Ball balancers are nonlinear, electromechanical, multivariable, open-loop unstable systems widely used in research laboratories, aerospace, military, and automotive industries to evaluate control mechanism effectiveness. The inherent difficulty in precisely managing ball position, combined with actuator saturation and system sensitivity to disturbances, makes trajectory tracking a persistent challenge. Conventional controllers often exhibit oscillatory responses with steady-state errors exceeding acceptable limits. Sliding mode control (SMC) offers robustness against model uncertainties; however, chattering finite-frequency, finite-amplitude oscillations near the sliding surface caused by switching imperfections, time delays, and actuator dynamics remain a significant limitation. This study addresses chattering through explicit vibration model compensation integrated into the SMC design for a 2-DOF ball balancer system using a visual servoing approach. A double-loop control architecture is implemented, where the inner loop handles servo angular position control and the outer loop manages ball position tracking through visual servoing feedback. The sliding mode controller is designed with a power rate reaching law, synthesizing two control laws: one with explicit vibration model compensation incorporating damping and stiffness terms, and one without. Experimental validation confirmed that SMC with compensation achieved significantly reduced steady-state error (0.034 mm vs. 0.386 mm) and lower overshoot (3.95% vs. 13.81%) compared to the uncompensated variant, with chattering amplitude reduced by approximately 72%.

In the pursuit of advancing renewable energy technologies, optimizing wind turbine performance through sophisticated control systems is pivotal for addressing energy variability and enhancing system efficiency. This paper presents a novel hybrid fuzzy super-twisting sliding mode control (HFSTSMC) strategy for Doubly Fed Induction Generators (DFIGs) used in modern wind turbine configurations. DFIGs are favored for their adaptability and efficiency in managing variable wind speeds, making them integral to sustainable energy solutions. The proposed HFSTSMC integrates the robustness of super-twisting sliding mode control (STSMC) with the adaptive capabilities of fuzzy logic control (FLC), aiming to mitigate the chattering effect inherent in traditional sliding mode controls while maintaining high tracking accuracy and robustness. The hybrid controller utilizes fuzzy logic to dynamically adjust control gains based on system states, enhancing the dynamic performance and stability of DFIG-based wind turbines. Extensive modeling and simulation using MATLAB/SIMULINK validate the effectiveness of HFSTSMC in improving power quality, reducing mechanical stresses, and optimizing energy capture. The results demonstrate significant improvements in system response to rapid wind condition changes and overall operational efficiency under transient and uncertain conditions. This research underscores the substantial benefits of implementing HFSTSMC, highlighting its potential to revolutionize wind turbine control systems and contribute significantly to the global transition towards reliable and efficient renewable energy sources. In the pursuit of advancing renewable energy technologies, optimizing wind turbine performance through sophisticated control systems is pivotal for addressing energy variability and enhancing system efficiency. This paper presents a novel hybrid fuzzy super-twisting sliding mode control (HFSTSMC) strategy for Doubly Fed Induction Generators (DFIGs) used in modern wind turbine configurations. DFIGs are favored for their adaptability and efficiency in managing variable wind speeds, making them integral to sustainable energy solutions. The proposed HFSTSMC integrates the robustness of super-twisting sliding mode control (STSMC) with the adaptive capabilities of fuzzy logic control (FLC), aiming to mitigate the chattering effect inherent in traditional sliding mode controls while maintaining high tracking accuracy and robustness. The hybrid controller utilizes fuzzy logic to dynamically adjust control gains based on system states, enhancing the dynamic performance and stability of DFIG-based wind turbines. Extensive modeling and simulation using MATLAB/SIMULINK validate the effectiveness of HFSTSMC in improving power quality, reducing mechanical stresses, and optimizing energy capture. The results demonstrate significant improvements in system response to rapid wind condition changes and overall operational efficiency under transient and uncertain conditions. This research underscores the substantial benefits of implementing HFSTSMC, highlighting its potential to revolutionize wind turbine control systems and contribute significantly to the global transition towards reliable and efficient renewable energy sources. In the pursuit of advancing renewable energy technologies, optimizing wind turbine performance through sophisticated control systems is pivotal for addressing energy variability and enhancing system efficiency. This paper presents a novel hybrid fuzzy super-twisting sliding mode control (HFSTSMC) strategy for Doubly Fed Induction Generators (DFIGs) used in modern wind turbine configurations. DFIGs are favored for their adaptability and efficiency in managing variable wind speeds, making them integral to sustainable energy solutions. The proposed HFSTSMC integrates the robustness of super-twisting sliding mode control (STSMC) with the adaptive capabilities of fuzzy logic control (FLC), aiming to mitigate the chattering effect inherent in traditional sliding mode controls while maintaining high tracking accuracy and robustness. The hybrid controller utilizes fuzzy logic to dynamically adjust control gains based on system states, enhancing the dynamic performance and stability of DFIG-based wind turbines. Extensive modeling and simulation using MATLAB/SIMULINK validate the effectiveness of HFSTSMC in improving power quality, reducing mechanical stresses, and optimizing energy capture. The results demonstrate significant improvements in system response to rapid wind condition changes and overall operational efficiency under transient and uncertain conditions. This research underscores the substantial benefits of implementing HFSTSMC, highlighting its potential to revolutionize wind turbine control systems and contribute significantly to the global transition towards reliable and efficient renewable energy sources.

This study proposes a novel power management strategy for wind farms using a grey wolf optimization (GWO)-based PI controller. The method aims to enhance active and reactive power control in systems employing dou bly fed induction generators. Three control strategies are evaluated—namely, a classical frequency-domain PI controller, an Artificial Neural Network (ANN)-based controller, and the proposed GWO-based PI controller—the last of which represents the main contribution. The classical PI and ANN controllers are included strictly for comparative bench marking. MATLAB simulations demonstrate that the GWO-beased PI controller offers superior dynamic performance, particularly in settling time and overshoot reduction. A power management algorithm is also developed to coordinate turbine outputs under varying wind conditions to meet a global production target. Results confirm the effectiveness of the proposed approach in enhancing wind farm stability, responsiveness, and energy efficiency.