Mongi Ben Moussa

This paper presents a hierarchical safe control approach for DC microgrids, combining learning-based energy management with rigorous constraint enforcement. The main novelty is a covariance-aware coupling mechanism that simultaneously informs CBF constraint tightening, sliding-mode gain scheduling, and DRL state augmentation. A continuous Soft Actor-Critic agent generates smooth current references and a flexible load-shedding ratio. A predictive CBF quadratic program acts as a safety filter, using online confidence intervals from a recursive least-squares predictor. The filter ensures a sampled-data safety property with an inter-sample margin, applying to the actual uncertain microgrid via a robust-CBF term, provided the QP is feasible with zero slack and the true voltage stays within the confidence interval. The same covariance information serves as an online uncertainty metric for adaptive gain and weight scheduling. A composite Lyapunov–CBF theorem proves uniform ultimate boundedness and constraint satisfaction. Simulations under severe conditions show high-probability constraint satisfaction with negligible slack, while improving transient recovery and reducing battery stress. Comparisons with fuzzy logic, discrete DQL, and plain SAC isolate the CBF contribution, and ablation studies assess individual components. While robust MPC can also enforce constraints, the proposed method achieves comparable satisfaction with lower complexity. A clear distinction is maintained between theoretical guarantees and simulation observations.

This paper develops a unified hybrid control architecture for switched nonlinear systems under aperiodic denial-of-service (DoS) attacks with non-minimum phase internal dynamics. The methodology relies on three technical pillars: multi-diffeomorphism input-output linearization, a mode-dependent event-triggered impulsive mechanism, and an attack-aware switching rule. The latter maximizes a resilience index during DoS-active intervals, while the event-triggered mechanism reduces communication updates, exploits mode information, and enables rapid correction after attacks. We derive constructive sufficient conditions based on AED-ADT and DoS parameters that guarantee ISS with an explicit exponential decay rate, and establish Zeno-free operation via a uniform positive lower bound on inter-event intervals. We also address the case where a DoS attack coincides with a switching instant and show that the architecture remains applicable. Validation via simulations on a nonholonomic mobile robot and a two-link robotic manipulator confirms superior convergence, resource efficiency, and robustness over baselines.

This paper addresses the voltage regulation problem for DC microgrids modeled as nonlinear dynamical systems subject to parametric uncertainties and external disturbances. A data-driven predictive hybrid control scheme is developed, combining a nonlinear sliding mode law that guarantees finite-time current convergence, an adaptive fuzzy universal approximator that compensates for unknown residual dynamics and mitigates chattering, and a recursive predictor built online via forgetting-factor recursive least squares. Real-time gain optimization is achieved through the minimization of a quadratic predictive performance index. A composite Lyapunov analysis rigorously establishes uniform ultimate boundedness of the low level Adaptive Fuzzy Sliding Mode Control (AFSMC) inner loop, assuming bounded reference currents provided by the DQL agent and characterizes the convergence residual set of the tracking error. Comparative simulations against conventional fuzzy logic and a standard (non augmented) Deep Q-Learning baseline with fixed gain SMC corroborate the theoretical guarantees, demonstrating superior voltage regulation, reduced battery deep discharges, and improved load management.

A hybrid control architecture is proposed for enhancing the stability and energy management of DC microgrids (DCMGs) integrating photovoltaic generation, batteries, and supercapacitors. The approach combines nonlinear Sliding Mode Control (SMC) for fast and robust DC bus voltage regulation with a Deep Q-Learning (DQL) agent that learns optimal high-level policies for charging, discharging, and load management. This dual-layer design leverages the real-time precision of SMC and the adaptive decision-making capability of DQL to achieve dynamic power sharing and balanced state-of-charge levels across storage units, thereby reducing asymmetric wear. Simulation results under variable operating scenarios showed that the proposed method significantly improvedvoltage stability, loweredthe occurrence of deep battery discharges, and decreased load shedding compared to conventional fuzzy-logic-based energymanagement, highlighting its effectiveness and resilience in the presence of renewable generation variability and fluctuating load demands.

This paper presents an intelligent design framework for a high-performance 26 GHz MIMO antenna array tailored to 5G applications, built upon a compact single-element patch. The 11.5 mm × 11.5 mm × 1.6 mm microstrip patch on FR4 exhibits near-unity electrical length, an ultra-deep return loss (S11 < −40 dB at 26 GHz), and a wide operational bandwidth from 24.4 to 31.2 GHz (6.8 GHz, ~26.2%). A two-element array, spaced at λ/2, is first augmented with a inspired metamaterial (MTM) unit cell whose dimensions are optimized via a Multi-Layer Perceptron (MLP) model to maximize gain (+2 dB) while preserving S11. In the second phase, a closed-square parasitic ring is introduced between the elements; its side length, thickness, and position are predicted by a Random Forest (RF) model with Bayesian optimization to minimize mutual coupling (S12) from −25 dB to −58 dB at 26 GHz without significantly degrading S11 (remains below −25 dB). Full-wave simulations and anechoic chamber measurements confirm the ML predictions. The close agreement among predicted, simulated, and measured S-parameters validates the efficacy of the proposed AI-assisted optimization methodology, offering a rapid and reliable route to next-generation millimeter-wave MIMO antenna systems.

This study introduces a novel control strategy tailored to nonlinear systems with non-minimum phase (NMP) characteristics. The framework leverages reinforcement learning within a cascade control architecture that integrates an Actor–Critic structure. Controlling NMP systems poses significant challenges due to the inherent instability of their internal dynamics, which hinders effective output tracking. To address this, the system is reformulated using the Byrnes–Isidori normal form, allowing the decoupling of the input–output pathway from the internal system behavior. The proposed control architecture consists of two nested loops: an inner loop that applies input–output feedback linearization to ensure accurate tracking performance, and an outer loop that constructs reference signals to stabilize the internal dynamics. A key innovation in this design lies in the incorporation of symmetry principles observed in both system behavior and control objectives. By identifying and utilizing these symmetrical structures, the learning algorithm can be guided toward more efficient and generalized policy solutions, enhancing robustness. Rather than relying on classical static optimization techniques, the method employs a learning-based strategy inspired by previous gradient-based approaches. In this setup, the Actor—modeled as a multilayer perceptron (MLP)—learns a time-varying control policy for generating intermediate reference signals, while the Critic evaluates the policy’s performance using Temporal Difference (TD) learning. The proposed methodology is validated through simulations on the well-known Inverted Pendulum system. The results demonstrate significant improvements in tracking accuracy, smoother control signals, and enhanced internal stability compared to conventional methods. These findings highlight the potential of Actor–Critic reinforcement learning, especially when symmetry is exploited, to enable intelligent and adaptive control of complex nonlinear systems.

This paper introduces a database-based design methodology aimed at optimizing a 26 GHz MIMO antenna system through machine learning (ML) techniques. The procedure is divided into two primary phases. Initially, a rectangular microstrip patch antenna is designed and enhanced using analytical models alongside ML algorithms that are trained on a detailed dataset of geometric parameters. This yields effective impedance matching (S11 < −45 dB) and a high gain (~6.64 dBi), which serve as the foundation for the MIMO structure. In the second phase, split ring resonator (SRR) configurations are integrated between the antenna elements to reduce mutual coupling. A specialized dataset, featuring varied dimensions of SRR, quantities of unit cells, and spatial placements, is utilized to train Random Forest models that forecast arrangements achieving optimal isolation (S21 < −40 dB) while maintaining low reflection losses. Additionally, a secondary dataset is constructed to investigate the best strategies for SRR placement, ensuring an optimal balance between isolation and return loss. The ultimate MIMO design is validated via comprehensive full-wave electromagnetic simulations and experimental measurements. The proposed system exhibits noteworthy performance enhancements, including an envelope correlation coefficient (ECC) < 0.005, diversity gain (DG) ≈ 9.99 dB, channel capacity loss (CCL) < 0.3 bits/s/Hz, total active reflection coefficient (TARC) < −30 dB, radiation efficiency exceeding 80%, and a maximum gain increase up to 10.22 dB. The close correlation between predicted and measured outcomes validates the effectiveness of the ML-driven approach in expediting antenna optimization for 5G and future applications.