In order to precisely control the wind power generation systems under nonlinear variable wind velocity, this paper proposes a novel maximum power tracking (MPPT) strategy for wind turbine systems based on a hybrid wind velocity forecasting algorithm. The proposed algorithm adapts the bat algorithm and improved extreme learning machine (BA-ELM) for forecasting wind speed to alleviate the slow response of anemometers and sensors, considering that the change of wind speed requires a very short response time. In the controlling strategy, to optimize the output power, a state feedback control technique is proposed to achieve the rotor flux and rotor speed tracking purpose based on MPPT algorithm. This method could decouple the current and voltage of induction generator to track the reference of stator current and flux linkage. By adjusting the wind turbine mechanical speed, the wind energy system could operate at the optimal rotational speed and achieve the maximal power. Simulation results verified the effectiveness of the proposed technique.

This paper proposes a framework integrating Rate Splitting Multiple Access (RSMA), semantic communications, and generative AI for optimizing next-generation wireless networks. We present a unified model that combines RSMA with semantic communications to enhance both spectral efficiency and semantic fidelity. Our system model focuses on a downlink semantic communication system with a multi-antenna base station serving multiple single-antenna users. A dynamic contract-based incentive mechanism is developed to address user heterogeneity and information asymmetry in semantic RSMA scenarios. We introduce a diffusion model-based approach for joint optimization of resource allocation and contract design in RSMA systems. The semantic encoding process extracts key information, i.e., free-space detection, interest points, object attributes, and spatial relationships, from image data. A loss function is designed to train the semantic encoder and RSMA scheme, incorporating semantic extraction, partitioning, reconstruction, and task-specific components. Our framework includes a multi-objective performance evaluation that considers both conventional metrics and semantic accuracy in a multi-user RSMA environment. We also define a multi-component semantic accuracy metric to assess the quality and utility of the extracted semantic information. Extensive simulation results demonstrate the superiority of our proposed framework over existing approaches in terms of system throughput, energy efficiency, and semantic fidelity across various network scenarios and user distributions.

This paper presents a novel adaptive twisting sliding mode control strategy combined with a speed-sensorless cascade nonlinear observer for the high-performance control of linear induction motors (LIMs). The primary objective is to achieve accurate speed and rotor flux tracking without relying on mechanical sensors, thereby enhancing system reliability and reducing hardware complexity. For this purpose, a cascade nonlinear observer is designed and applied to the class of nonlinear affine systems representing LIM dynamics. Based on the interconnected form of the LIM mathematical model, the observer simultaneously reconstructs both the motor speed and rotor fluxes in real time. The stability of the proposed cascade observer is analyzed using Lyapunov theory, ensuring the convergence of the estimation errors under bounded disturbances. Complementing the observer, two adaptive gain twisting sliding mode controllers are developed: one for speed tracking and another for flux regulation. These controllers are robust against external disturbances and parameter uncertainties, even when the bounds of such disturbances are unknown. This feature significantly enhances the practical applicability of the control system in real-world industrial environments. To validate the performance and robustness of the proposed control scheme, a hardware-in-the-loop (HIL) experiment was conducted. Comparative studies with existing state-of-the-art sensorless control methods demonstrate that the proposed cascade nonlinear observer-based approach achieves faster convergence, higher estimation accuracy, and better disturbance rejection capabilities, while requiring less computational effort.

This paper presents a twisting controller (TC) based on a high-order sliding mode observer (HOSMO) for linear induction motors (LIMs), accounting for dynamic end effects. Based on the LIM model in the field-oriented frame, two extended subsystems are developed: a velocity extended model and a flux extended model. Using these models, two HOSMOs are designed to estimate the disturbances in each subsystem. The HOSMO outputs are then used for disturbance rejection, resulting in two second-order systems with small bounded disturbances. Two TCs are subsequently implemented to achieve finite-time velocity and flux tracking of the LIM. The primary advantage of this strategy lies in its ability to reduce chattering through active disturbance rejection. Hardware-in-the-loop (HIL) experiments validate the effectiveness of the proposed TC-HOSMO scheme.