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.

Current multimodal neural machine translation (MNMT) approaches primarily focus on ensuring consistency between visual annotations and the source language, often overlooking the broader aspect of multimodal coherence, including target–visual and bilingual–visual alignment. In this paper, we propose a novel approach that effectively leverages target–visual consistency (TVC) and bilingual–visual consistency (BiVC) to improve MNMT performance. Our method leverages visual annotations depicting concepts across bilingual parallel sentences to enhance multimodal coherence in translation. We exploit target–visual harmony by extracting contextual cues from visual annotations during auto-regressive decoding, incorporating vital future context to improve target sentence representation. Additionally, we introduce a consistency loss promoting semantic congruence between bilingual sentence pairs and their visual annotations, fostering a tighter integration of textual and visual modalities. Extensive experiments on diverse multimodal translation datasets empirically demonstrate our approach’s effectiveness. This visually aware, data-driven framework opens exciting opportunities for intelligent learning, adaptive control, and robust distributed optimization of multi-agent systems in uncertain, complex environments. By seamlessly fusing multimodal data and machine learning, our method paves the way for novel control paradigms capable of effectively handling the dynamics and constraints of real-world multi-agent applications.

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 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.

Linear Induction Motor (LIM) is widely used in rail transit, precision positioning, and industrial automation due to its high efficiency, simple structure, and low maintenance cost. However, the dynamic performance of linear induction motors is affected by nonlinear characteristics and parameter variations, making accurate state and speed estimation crucial for achieving higher performance control. Traditional estimation methods often have limitations and are difficult to meet the increasing demand for control accuracy. In this paper, an adaptive higher-order sliding mode observer (AHOSMO) based synchronous state and speed estimation method for linear induction motors is proposed to address the shortcomings of traditional estimation methods in terms of accuracy and robustness. Considering the boundary effect of linear induction motors, a higher-precision mathematical model is established. Based on the above model, the proposed observer combines the adaptive law and the higher-order sliding mode method to estimate the rotor fluxes and LIM speed, as well as the speed-dependent parameters $\gamma,\alpha,\beta,\varsigma,\eta,\delta$. The application of a super-twisting algorithm (STA) guarantees finite-time convergence for LIM state variable estimation. By employing the higher-order sliding mode algorithm, continuous inputs can be generated without the chattering phenomenon. Numerical simulation and experimental results show that the proposed AHOSMO can quickly and accurately estimate the synchronous state and velocity of the LIM under different operating conditions. Compared with existing methods, the AHOSMO demonstrates significant advantages in estimation accuracy and anti-interference capability. The research results provide a new technical approach for higher-performance control of linear induction motors, which is of great significance for promoting their application in the field of high-precision positioning.