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Actuators, Volume 14, Issue 9 (September 2025) – 4 articles

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15 pages, 5048 KiB  
Article
Master Cylinder Pressure Control Based on Piecewise-SMC in Electro-Hydraulic Brake System
by Cong Liang, Xing Xu, Hui Deng, Chuanlin He, Long Chen and Yan Wang
Actuators 2025, 14(9), 416; https://doi.org/10.3390/act14090416 - 24 Aug 2025
Abstract
This paper focuses on enhancing master cylinder pressure control in pressure-sensorless Electro-Hydraulic Brake (EHB) systems. A novel control strategy is developed, integrating a Piecewise Sliding Mode Controller (Piecewise-SMC) with an Extended Sliding Mode Observer (ESMO) based on a newly derived pressure–position–velocity model that [...] Read more.
This paper focuses on enhancing master cylinder pressure control in pressure-sensorless Electro-Hydraulic Brake (EHB) systems. A novel control strategy is developed, integrating a Piecewise Sliding Mode Controller (Piecewise-SMC) with an Extended Sliding Mode Observer (ESMO) based on a newly derived pressure–position–velocity model that accounts for rack position and velocity effects. To handle external disturbances and parameter uncertainties, the ESMO provides accurate pressure estimation. The nonlinear EHB model is approximated piecewise linearly to facilitate controller design. The proposed Piecewise-SMC regulates motor torque to achieve precise pressure tracking. Experimental validation under step-change braking conditions demonstrates that the Piecewise-SMC reduces response time by 31.8%, overshoot by 35.8%, and tracking root mean square error by 9.6% compared to traditional SMC, confirming its effectiveness and robustness for pressure-sensorless EHB applications. Full article
24 pages, 1543 KiB  
Article
Intelligent Fault Diagnosis for Rotating Machinery via Transfer Learning and Attention Mechanisms: A Lightweight and Adaptive Approach
by Zhengjie Wang, Xing Yang, Tongjie Li, Lei She, Xuanchen Guo and Fan Yang
Actuators 2025, 14(9), 415; https://doi.org/10.3390/act14090415 - 23 Aug 2025
Abstract
Fault diagnosis under variable operating conditions remains challenging due to the limited adaptability of traditional methods. This paper proposes a transfer learning-based approach for bearing fault diagnosis across different rotational speeds, addressing the critical need for reliable detection in changing industrial environments. The [...] Read more.
Fault diagnosis under variable operating conditions remains challenging due to the limited adaptability of traditional methods. This paper proposes a transfer learning-based approach for bearing fault diagnosis across different rotational speeds, addressing the critical need for reliable detection in changing industrial environments. The method trains a diagnostic model on labeled source-domain data and transfers them to unlabeled target domains through a two-stage adaptation strategy. First, only the source-domain data are labeled to reflect real-world scenarios where target-domain labels are unavailable. The model architecture combines a convolutional neural network (CNN) for feature extraction with a self-attention mechanism for classification. During source-domain training, the feature extractor parameters are frozen to focus on classifier optimization. When transferring to target domains, the classifier parameters are frozen instead, allowing the feature extractor to adapt to new speed conditions. Experimental validation on the Case Western Reserve University bearing dataset (CWRU), Jiangnan University bearing dataset (JNU), and Southeast University gear and bearing dataset (SEU) demonstrates the method’s effectiveness, achieving accuracies of 99.95%, 99.99%, and 100%, respectively. The proposed method achieves significant model size reduction compared to conventional TL approaches (e.g., DANN and CDAN), with reductions of up to 91.97% and 64%, respectively. Furthermore, we observed a maximum reduction of 61.86% in FLOPs consumption. The results show significant improvement over conventional approaches in maintaining diagnostic performance across varying operational conditions. This study provides a practical solution for industrial applications where equipment operates under non-stationary speeds, offering both computational efficiency and reliable fault detection capabilities. Full article
(This article belongs to the Section Actuators for Manufacturing Systems)
21 pages, 3369 KiB  
Article
Event-Triggered Fixed-Time Consensus Tracking Control for Uncertain Nonlinear Multi-Agent Systems with Dead-Zone Input
by Zian Wang, Yixiang Gu, Jiarui Liu, Yue Zhang, Kai Feng, Jietao Dai and Guoxiong Zheng
Actuators 2025, 14(9), 414; https://doi.org/10.3390/act14090414 - 22 Aug 2025
Abstract
This study explores the issue of fixed-time dynamic event-triggered consensus control for uncertain nonlinear multi-agent systems (MASs) within directed graph frameworks. In practical applications, the system encounters multiple constraints such as unknown time-varying parameters, unknown external disturbances, and input dead zones, which may [...] Read more.
This study explores the issue of fixed-time dynamic event-triggered consensus control for uncertain nonlinear multi-agent systems (MASs) within directed graph frameworks. In practical applications, the system encounters multiple constraints such as unknown time-varying parameters, unknown external disturbances, and input dead zones, which may increase the communication burden of the system. Therefore, achieving fixed-time consensus tracking control under the aforementioned conditions is challenging. To address these issues, an adaptive fixed-time consensus tracking control method based on boundary estimation and fuzzy logic systems (FLSs) is proposed to achieve online compensation for the input dead zone. Additionally, to optimize the utilization of communication resources, a periodic adaptive event-triggered control (PAETC) is designed. The mechanism dynamically adjusts the frequency at which the trigger is updated in real time, reducing communication resource usage by responding to changes in the control signal. Finally, the efficacy of the proposed approach is confirmed via theoretical evaluation and simulation. Full article
(This article belongs to the Special Issue Analysis and Design of Linear/Nonlinear Control System)
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27 pages, 8503 KiB  
Article
Design and Implementation of an Autonomous Intelligent Fertigation System for Cross-Regional Applications
by Ruizhi Tang, Hanhong Hu, Hai Lin, Jiahao Li, Zian Wang, Guanquan Zhu, Ziyou Mei and Jietao Dai
Actuators 2025, 14(9), 413; https://doi.org/10.3390/act14090413 - 22 Aug 2025
Abstract
Conventional fertigation systems suffer from limited cross-regional adaptability, mainly due to unstable fertilizer flow from fixed-aperture units, poor terrain adaptability, and an inadequate response to environmental heterogeneity. This study proposes an autonomous, cross-regional intelligent fertigation system based on an STM32F1 microcontroller and UART [...] Read more.
Conventional fertigation systems suffer from limited cross-regional adaptability, mainly due to unstable fertilizer flow from fixed-aperture units, poor terrain adaptability, and an inadequate response to environmental heterogeneity. This study proposes an autonomous, cross-regional intelligent fertigation system based on an STM32F1 microcontroller and UART communication protocols. The system integrates a mechanically adjustable iris fertilizer delivery unit, a dual-axis fertigation module, a data interconnection unit, and comprehensive control software with dynamic calibration capabilities. Prototype evaluations conducted on both sloped terrain (up to 38°) and flat surfaces demonstrate a stable performance, achieving fertilizer flow control errors below 3%, irrigation deviation under 5%, and fertilization deviation within 2%. Real-time data acquisition, remote monitoring, and intelligent operation are supported by a YOLOv5s-based visual recognition system, which attains an mAP@0.5 of 92.5%. This integrated solution offers a robust approach to precision agriculture across diverse environmental conditions. Full article
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