This article presents novel artificial intelligence (AI)‐based techniques for controlling wind energy conversion systems, specifically fuzzy logic control and neural networks, known as fuzzy hysteresis‐direct power control (FH‐DPC) and neural hysteresis‐DPC (NH‐DPC), respectively. The primary purpose is to overcome conventional DPC (C‐DPC) limitations in doubly fed induction generator wind turbines (WT‐DFIG), focusing on power quality improvement and enhanced system efficiency. The techniques aim to reduce power ripples and improve the quality of alternating current (AC) grid energy by improving current signal quality in all WTs’ operation modes with WT‐DFIG and all compensation power modes. The suggested techniques are thoroughly examined using the MATLAB/Simulink environment under various wind scenarios, demonstrating a reduction in active power ripples by over 70%, a reduction in reactive power ripples of around 77% on average, a decrease in generated current total harmonic distortions (THDs) by over 70% compared to C‐DPC. The performances of FH‐DPC and NH‐DPC are contrasted with C‐DPC and other previously suggested methods, and it is concluded that the proposed control approaches perform more effectively than them regarding ripples in local reactive compensation and generated active powers as well as THD currents, with FH‐DPC slightly outperforming NH‐DPC. The research indicates that AI can enhance the effectiveness and quality of power generated by wind‐power systems.

This paper presents an adaptive fault-tolerant control strategy tailored for fixed-wing unmanned aerial vehicles (UAV) operating under adverse conditions such as icing. Using radial basis function neural networks and nonlinear dynamic inversion, the proposed framework effectively handles simultaneous actuator and sensor faults with arbitrary nonlinear dynamics caused by environmental effects, model uncertainties and external disturbances. A nonlinear disturbance observer is incorporated for accurate sensor fault detection and estimation, thereby enhancing the robustness of the control system. The integration of the radial basis function neural network enables an adaptive estimation of the faults, ensuring accurate fault compensation and system stability under challenging conditions. The observer is optimised to minimise the deviation of the closed-loop dynamics eigenvalues from the assigned eigenvalues and to approach unity observer steady-state gain. The stability of the control architecture is mathematically proven using Lyapunov analysis, and the performance of the approach is validated through numerical simulations on a six Degrees of Freedom fixed-wing unmanned aerial vehicles model. The results show superior performance and robustness to challenging fault scenarios. This research provides a comprehensive fault management solution that enhances the safety and reliability of unmanned aircraft operations in extreme environments.