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The guidance laws for intercepting maneuvering targets in three‐dimensional (3D) space poses considerable challenges owing to various inescapable factors. These factors include the impact angle, input saturation constraints, and uncertainty. To address these challenges, a novel universal operator, denoted as |||·||| is introduced for the first time. This operator, when employed to represent sliding surface vectors, demonstrates a closer alignment with practical scenarios compared to the traditional Euclidean vector norm ||·||. Following this, a novel universal fixed‐time nonsingular terminal sliding surface is introduced in both scalar and vector representations, effectively resolving issues related to singular points and achieving reduced convergence times. Additionally, Furthermore, a new continuous adaptive finite‐time nonsingular terminal sliding mode guidance law (CAFnTNTSMGL) has been formulated. This guidance law incorporates a newly proposed sliding surface, a modified finite‐time super‐twisting algorithm, and a parameter‐adaptive law. The system's stability and its finite convergence time are subsequently demonstrated. Finally, the effectiveness of CAFnTNTSMGL is validated through a comparative analysis of simulation results. CAFnTNTSMGL has the capacity to effectively mitigate the negative effects resulting from the indeterminate upper limit of the overall uncertainty has less intercept time, smaller terminal line‐of‐sight (LOS) angle error, smaller maximum field of view, and smaller total cost of energy. The article introduces novel operator III.III, innovative non‐singular sliding surface, and advanced guidance law. A comparative analysis of theoretical and numerical simulations against four alternative methodologies has yielded favorable outcomes.

This research focuses on the development of a robust modified superior-order sliding mode controller (MSOSMC) based on the neural super-twisting algorithm (NSTA) for application in wind turbine conversion systems (WTCS) employing the doubly powered induction generator (DPIG). The study is conducted under real conditions in the Adrar region of Algeria. The optimal controller parameters are determined using the grey wolf optimizer (GWO) and are compared with results obtained using particle swarm optimization (PSO). In the DPIG system, the stator is directly connected to the main grid, while the rotor is linked to the grid through a back-to-back inverter. The research addresses the challenges and constraints associated with using individual superior-order sliding mode strategy, particularly the ripple caused by the discontinuous low (sign section) of conventional SOSMC, which can affect energy quality, and the materials used in wind conversion systems. The neural super-twisting algorithm (NSTA) integrated into the SOSMC is employed to maximize wind energy extraction and mitigate ripple issues. Furthermore, it contributes to improving power supply quality, especially in the presence of parametric variations. Simulation tests validate the effectiveness of the proposed approach (MSOSM/NSTA) using GWO, showcasing enhanced robustness and performance compared to conventional SOSMC and sliding mode first order (SMC1), particularly in addressing ripple problems. The study also assesses the system's response to wind speed fluctuations and its high robustness against changes in machine parameters.

Precision trajectory tracking problem for Unmanned Aerial Systems (UAS) is addressed in this work. A novel algorithm that combines a Nonsingular Modified Super-Twisting Controller with a High Order Sliding Mode Observer to enable an aerial vehicle tracking a desired trajectory under the assumption that i) its translational velocities are not available and ii) there are unmodeled dynamics and external disturbances. The proposed Sliding Mode Controller is based on a nonlinear sliding mode surface that ensures that the position and velocity tracking errors of all system’s state variables converge to zero in finite time. Moreover, the proposed controller generates a continuous control signal eliminating the chattering phenomenon. Finally, simulation results and an extensive set of experiments are presented in order to illustrate the robustness and effectiveness of the proposed control strategy.

In response to the need for cost-effective and resilient drivetrain architectures in renewable energy emulation platforms, this paper proposes a wind turbine emulator (WTE) designed to enhance the operational efficiency of variable-speed wind turbines (WTs) connected to electric generators in power grid applications. The proposed emulator relies on a robust sensorless vector-controlled induction motor (IM) drive fed by a reduced-switch soft–voltage source inverter (Soft-VSI) topology. The proposed control chain combines a second-order super-twisting sliding-mode flux observer, based on stator measurements, with a modified MRAS speed estimator whose Popov hyperstability offers explicit PI tuning and ensures stable sensorless speed convergence. The complete WTE design, from the aerodynamic model to the Soft-VSI induction motor drive, is implemented and evaluated in MATLAB/Simulink environment. A Mexican hat wind speed profile is used to excite the emulator and assess its dynamic behavior under diverse transient conditions. The simulation results demonstrate fast convergence of the estimated flux and speed, stable closed-loop operation when using the estimated speed, and strong robustness against no-loaded and loaded operations and rotor-resistance variations. Moreover, a comparative analysis between the proposed control scheme and a conventional first-order sliding-mode flux observer is carried out to highlight the enhanced flux and speed estimation accuracy, reduced chattering, and improved dynamic robustness of the WTE. The proposed framework provides a flexible tool to support the energy transition through the development of advanced wind energy system control strategies.