To address the inherent trade-off between transient performance and noise robustness of the traditional linear extended state observer (LESO) within the active disturbance rejection control (ADRC) framework, a novel reset extended state observer (RESO) scheme is proposed. Its core idea is to introduce a state correction mechanism regulated by the reset gain matrix into the LESO, which can maintain structural simplicity to the greatest extent. The reset action is activated only when the output estimation error undergoes a significant zero-crossing with the minimum reset interval constraint, thus achieving on-demand reset and avoiding Zeno behaviour. The closed-loop error dynamics model comprising flow dynamics and jump dynamics is established, and its stability is rigorously proven via the Lyapunov method. Furthermore, a systematic linear matrix inequality (LMI)-based procedure is developed for designing the reset gain matrix. Comparative simulations on a mass-spring-damper (MSD) system demonstrate that the proposed RESO achieves superior transient performance and noise robustness relative to the conventional LESO.
The proposed work presents a comprehensive investigation into the design, analysis, and performance assessment of a DC microgrid incorporating Interleaved Boost converter [IBC], a Photovoltaic (PV) system, battery, and a single-phase grid connection. This study focuses on the utilization of an innovative Interleaved Boost K-type converter to integrate the PV system with the DC bus. A novel Spotted Hyaena Optimized Adaptive Neuro-Fuzzy Inference System (SHO-ANFIS) controller is employed to retain a constant output voltage from the converter. Furthermore, the integration of battery storage into the DC microgrid is achieved through a dedicated battery converter, which enhances the system's flexibility. The microgrid connection to the single-phase grid is established using a single-phase Voltage Source Inverter (VSI). Moreover, the integration of Internet of Things (IoT) ensures the live monitoring and acquisition of crucial parameters within the DC microgrid. The effectiveness and robustness of the suggested converter structure are examined over comprehensive MATLAB models and validated with a prototype.
This article focuses on voltage and frequency profile stabilization in a self-excited induction generator-based hybrid system without using any battery energy storage unit. Eliminating the use of storage units significantly reduces the system cost and maintenance. Although this may reduce reliability, this work proposes an electric spring-based control strategy that employs a nine-switch converter and a reactive power management scheme that can provide reliability comparable to systems with energy storage units. The proposed system's performance has been evaluated during the day and night. During daytime conditions, when both photovoltaic and self-excited induction generators contribute to generation, the electric spring stabilizes the system's voltage and frequency. At night, power is generated only by the self-excited induction generator, and the system remains mostly in a power shortage. The proposed reactive power management scheme increases the utilization of the NSC during the night and improves the non-critical load voltage profile by nearly 28 V, while reducing the reactive power burden on the self-excited induction generator, resulting in an excess of 1.75 kW. On the other hand, the frequency profile remains nearly constant with a deviation of ±3%. The system is dynamically modelled and validated in real-time using an OPAL-RT 4510 platform.
Automated recognition of scalp-related disorders is challenging because fine-grained similarities among conditions are often obscured by lighting variations, skin-tone diversity, and image noise. Traditional examination relies heavily on clinical expertise and may produce inconsistencies in complex cases. To overcome these limitations, this study proposes a deep learning system integrating contrast-sensitive enhancement, structural edge detection, hierarchical feature encoding, and adaptive classification for precise scalp analysis. Initially, dynamic contrast enhancement amplifies hidden textures and tonal variations in scalp images. Laplacian of Gaussian (LoG) filtering subsequently sharpens intensity transitions and delineates boundary regions essential for identifying subtle abnormalities. The preprocessed images are passed through a customized VGG-16 network to extract multiscale features while preserving low-level patterns and high-level structural cues. A Stacked Convolutional Sparse Autoencoder Network (SCSAEN) compresses and reconstructs these features to obtain compact, noise-resistant representations for classification. The Secretary Bird Optimization Algorithm (SBOA) fine-tunes the model parameters, improving convergence and generalization across diverse inputs. Compared with existing approaches, the proposed model achieved superior diagnostic performance, attaining 99.80% accuracy, 97.64% precision, 96.81% recall, and 98.81% specificity. It also obtained a Dice coefficient of 98.12% and completed the processing cycle within 180.4 s, demonstrating its potential for accurate and efficient clinical decision support.
This paper investigates the challenges of event-triggered trajectory tracking control for multiple high-speed train nonlinear systems subject to model uncertainty. First, a neural network-based minimum learning parameter (MLP) method is proposed for multi-train nonlinear systems with model uncertainty and integrated into the controller design, eliminating conventional neural network weight selection. Second, to reduce the communication resource burden, a fixed-threshold event-triggered mechanism is developed. Finally, an event-triggered adaptive tracking control approach based on the MLP method is presented within the sliding mode control framework. Theoretical analysis demonstrates that all closed-loop control signals are uniformly ultimately bounded and that Zeno behaviour is avoided. Simulation results verify the effectiveness of the proposed control scheme.
