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The Small Frontier: Trends Toward Miniaturization and the Future of Planetary Surface Rovers
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A Hybrid Flying Robot Utilizing Water Thrust and Aerial Propellers: Modeling and Motion Control System Design
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Advances in Langevin Piezoelectric Transducer Designs for Broadband Ultrasonic Transmitter Applications
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A Review of Bio-Inspired Actuators and Their Potential for Adaptive Vehicle Control
Journal Description
Actuators
Actuators
is an international, peer-reviewed, open access journal on the science and technology of actuators and control systems published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within SCIE (Web of Science), Scopus, Inspec, and other databases.
- Journal Rank: JCR - Q2 (Engineering, Mechanical) / CiteScore - Q1 (Control and Optimization)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 19 days after submission; acceptance to publication is undertaken in 1.9 days (median values for papers published in this journal in the first half of 2025).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Journal Cluster of Instruments and Instrumentation: Actuators, AI Sensors, Instruments, Micromachines and Sensors.
Impact Factor:
2.3 (2024);
5-Year Impact Factor:
2.4 (2024)
Latest Articles
An Event-Triggered Observer-Based Control Approach for Enhancing Resilience of Cyber–Physical Systems Under Markovian Cyberattacks
Actuators 2025, 14(8), 412; https://doi.org/10.3390/act14080412 - 21 Aug 2025
Abstract
This paper presents a resilient observer-based and event-triggered control scheme for discrete-time Cyber–Physical Systems (CPS) under Markovian Cyber-Attacks (MCA). The proposed framework integrates a Luenberger observer for cyberattack detection with a state-feedback controller designed to preserve system stability in the presence of Denial-of-Service
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This paper presents a resilient observer-based and event-triggered control scheme for discrete-time Cyber–Physical Systems (CPS) under Markovian Cyber-Attacks (MCA). The proposed framework integrates a Luenberger observer for cyberattack detection with a state-feedback controller designed to preserve system stability in the presence of Denial-of-Service (DoS) and False Data Injection (FDI) attacks. Attack detection is achieved through residual signal generation combined with Markovian modeling of the attack dynamics. System stability is guaranteed by formulating relaxed Linear Matrix Inequality (LMI) conditions that incorporate relaxation variables, a diagonal Lyapunov function, the S-procedure, and congruence transformations. Moreover, the Event-Triggered Mechanism (ETM) efficiently reduces communication load without degrading control performance. Numerical simulations conducted on a three-tank system benchmark confirm enhanced detection accuracy, faster recovery, and strong robustness against uncertainties.
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(This article belongs to the Special Issue Actuator Technologies and Control: Materials, Devices and Applications)
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Open AccessArticle
Nonlinearity Characterization of Flexible Hinge Piezoelectric Stages Under Dynamic Preload via a Force-Dependent Prandtl–Ishlinskii Model with a Force-Analyzed Finite Element Method
by
Xuchen Wang, Dong An, Zicheng Qin, Chuan Wang, Yuping Liu and Yixiao Yang
Actuators 2025, 14(8), 411; https://doi.org/10.3390/act14080411 - 19 Aug 2025
Abstract
The operational performance of Flexible Hinge Piezoelectric Stages (FHPSs), essential components in precision engineering, is fundamentally constrained by the inherent hysteresis of the piezoelectric actuator (PEA). A significant deficiency in prevailing characterization methods is their failure to consider the dynamic nature of the
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The operational performance of Flexible Hinge Piezoelectric Stages (FHPSs), essential components in precision engineering, is fundamentally constrained by the inherent hysteresis of the piezoelectric actuator (PEA). A significant deficiency in prevailing characterization methods is their failure to consider the dynamic nature of the mechanical preload exerted by the flexible hinge. This position-dependent preload induces substantial deviations in the PEA’s response characteristics, thereby compromising the predictive accuracy of conventional design frameworks. To address this limitation, this paper proposes a Force-Dependent Prandtl–Ishlinskii (FPI) model that explicitly formulates the PEA’s hysteretic behavior as a function of variable preload conditions. The FPI model is subsequently integrated into a comprehensive FPI-FFEM characterization framework. Within this framework, a Force-analyzed Finite Element Method (FFEM) is utilized to compute the dynamic preload throughout the actuator’s operational stroke. This information, notably neglected in conventional FEM analysis, is essential to the fidelity of the proposed FPI model. Experimental validation demonstrates the superior fidelity of the FPI model in comparison to the traditional PI model for tracking preload-induced nonlinearities. Furthermore, the complete FPI-FFEM framework exhibits substantially enhanced prediction accuracy relative to both conventional PI-FEM and advanced LDPI-FEM methodologies, as demonstrated by a significant reduction in the Mean Absolute Error (MAE).
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(This article belongs to the Special Issue Piezoelectric Actuators and Motors: State-of-the-Art and Perspectives for Actuators)
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Open AccessArticle
Intelligent Soft Sensors for Inferential Monitoring of Hydrodesulfurization Process Analyzers
by
Željka Ujević Andrijić, Srečko Herceg, Magdalena Šimić and Nenad Bolf
Actuators 2025, 14(8), 410; https://doi.org/10.3390/act14080410 - 19 Aug 2025
Abstract
This work presents the development of soft sensor models for monitoring the operation of online process analyzers used to measure the sulfur content in the product of the refinery hydrodesulfurization process. Since sulfur content often fluctuates over time, soft sensor models must account
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This work presents the development of soft sensor models for monitoring the operation of online process analyzers used to measure the sulfur content in the product of the refinery hydrodesulfurization process. Since sulfur content often fluctuates over time, soft sensor models must account for these frequency fluctuations. We have therefore developed dynamic data-driven models based on linear and nonlinear system identification techniques (finite impulse response—FIR, autoregressive with exogenous inputs—ARX, output error—OE, nonlinear ARX—NARX, Hammerstein–Wiener—HW) and machine learning techniques, including models based on long short-term memory (LSTM) and gated recurrent unit (GRU) networks, as well as artificial neural networks (ANNs). The core steps in model development included the selection and preprocessing of continuously measured plant process data, collected from a full-scale industrial hydrodesulfurization unit under normal operating conditions. The developed soft sensor models are intended to support or replace process analyzers during maintenance periods or equipment failures. Moreover, these models enable the application of inferential control strategies, where unmeasured process variables—such as sulfur content—can be estimated in real time and used as feedback for advanced process control.
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(This article belongs to the Special Issue Analysis and Design of Linear/Nonlinear Control System)
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Open AccessArticle
Servo State-Based Polynomial Interpolation Model Predictive Control for Enhanced Contouring Control
by
Shisheng Lv, Qiang Liu, Yiqing Yang, Yanqiang Liu, Liuquan Wang, Chenxin Zang and Zhiwei Ning
Actuators 2025, 14(8), 409; https://doi.org/10.3390/act14080409 - 19 Aug 2025
Abstract
To further improve machining accuracy under the constrained conditions of multi-axis dynamic response, current research is focusing on the control of CNC machine toolpaths, with contour error as the target. While extant approaches analyze positions solely at PLC sampling nodes, they neglect inter-sample
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To further improve machining accuracy under the constrained conditions of multi-axis dynamic response, current research is focusing on the control of CNC machine toolpaths, with contour error as the target. While extant approaches analyze positions solely at PLC sampling nodes, they neglect inter-sample toolpath fluctuations induced by velocity deviations. This paper proposes a servo state-based polynomial interpolation model predictive control that predicts real-time toolpath behavior by utilizing servo axis states. The polynomial interpolation of servo states (e.g., position/velocity feedback) enables high-fidelity toolpath prediction between PLC nodes, overcoming the limitation imposed by the sampling gap. Experimental validation on a five-axis motion platform with S-shaped trajectories demonstrates that, without extending the prediction horizon of the model predictive control method, the proposed method reduces contour error by approximately 20% at the tool tip and 40% in tool orientation, while decreasing contour error fluctuations by around 60% compared to conventional model predictive control method.
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(This article belongs to the Section Control Systems)
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Open AccessSystematic Review
A Systematic Review on Smart Insole Prototypes: Development and Optimization Pathways
by
Vítor Miguel Santos, Beatriz B. Gomes, Maria Augusta Neto, Patrícia Freitas Rodrigues and Ana Martins Amaro
Actuators 2025, 14(8), 408; https://doi.org/10.3390/act14080408 - 15 Aug 2025
Abstract
This review synthesizes research on smart insole prototypes and their designs, focusing on those incorporating artificial intelligence (AI) and a wireless communication/transmission system. The main objective of this work is to summarize existing studies, identify key trends, evaluate the performance of these innovative
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This review synthesizes research on smart insole prototypes and their designs, focusing on those incorporating artificial intelligence (AI) and a wireless communication/transmission system. The main objective of this work is to summarize existing studies, identify key trends, evaluate the performance of these innovative biomechanical tools, and recognize the factors that could lead to optimization. This comprehensive analysis includes studies from PubMed, Scopus, and Web of Science databases and other investigations on the critical themes to consider. It follows strict inclusion and exclusion criteria, ensuring the quality and accuracy of the overview. The findings emphasize significant progress in smart insoles, particularly in AI-enhanced prototypes, while addressing existing challenges and problems. This review helps guide potential future research and define practical application directions. The growing importance of biomechanics, especially on smart insoles, underscores the considerable potential of these innovations to monitor and improve human movement in both clinical and non-clinical settings, promising a future of more effective and personalized health and performance interventions. This protocol was registered with the International Platform of Registered Systematic Review and Meta-Analysis Protocols (INPLASY) on 6 February 2025 and was last updated on 6 February 2025.
Full article
(This article belongs to the Special Issue Actuator Technologies and Control: Materials, Devices and Applications)
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Open AccessArticle
Robustness Analysis of the Model Predictive Position Control of an Electro-Mechanical Actuator for Primary Flight Surfaces
by
Marco Lucarini, Gianpietro Di Rito, Marco Nardeschi and Nicola Borgarelli
Actuators 2025, 14(8), 407; https://doi.org/10.3390/act14080407 - 14 Aug 2025
Abstract
This paper deals with the design and the robustness analysis of a model predictive control (MPC) for the position tracking of primary flight movables driven by electro-mechanical actuators. This study is, in particular, focused on a rotary electro-mechanical actuator (EMA) by UMBRAGROUP, employing
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This paper deals with the design and the robustness analysis of a model predictive control (MPC) for the position tracking of primary flight movables driven by electro-mechanical actuators. This study is, in particular, focused on a rotary electro-mechanical actuator (EMA) by UMBRAGROUP, employing a patented mechanical transmission based on a differential ball-screw mechanism characterized by a huge gear ratio. To obtain a baseline reference, conventional PID regulators were initially optimized by using multi-objective cost functions based on tracking accuracy, load disturbance rejection, and power consumption. The position regulator was then replaced by an MPC regulator, designed to balance performance, computational resources, and safety constraints. A nonlinear physics-based simulation model of the EMA, entirely developed in the Matlab–Simulink environment and validated with experiments, was used to compare the two control strategies. The simulation results in both the time and frequency domains highlight that the MPC solution provides faster and more accurate position tracking, improved dynamic stiffness, and reduced power absorption. Finally, the robustness against model uncertainties of the MPC was addressed by imposing random and combined deviations of model parameters from the nominal values (via Monte Carlo analysis). The results demonstrate that the implementation of MPC control laws could enhance the stability and the reliability of EMAs, thus supporting their application for safety-critical flight control functions.
Full article
(This article belongs to the Special Issue Flight Control Systems and Dynamic Simulation for Aerospace Applications)
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Open AccessArticle
A Data-Driven Global Load Case Analysis Method for Aircraft Structural Design
by
Yongbin Liu, Kaiyi Zheng, Xichang Liang and Qianqian Xin
Actuators 2025, 14(8), 406; https://doi.org/10.3390/act14080406 - 13 Aug 2025
Abstract
Aircraft encounter complex ground and air scenarios during service, necessitating a comprehensive analysis of extensive global load cases during the design phase to ensure structural reliability and safety. While high-fidelity finite element analysis enables precise assessment of load case criticality, its prohibitive human
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Aircraft encounter complex ground and air scenarios during service, necessitating a comprehensive analysis of extensive global load cases during the design phase to ensure structural reliability and safety. While high-fidelity finite element analysis enables precise assessment of load case criticality, its prohibitive human and computational costs constrain aircraft iterative development. To overcome this challenge, this study proposes a Global Load Case Analysis (GLCA) system for identifying critical load cases across structural sections. The method is driven by aerodynamic load data and structural response data from coarse-grid models. First, it achieves a quantitative ranking of global load case criticality, providing engineers with a standardized severity metric. Second, based on defined criticality relationships, it identifies coverage, coupling, and differentiation patterns among load cases to establish criticality hierarchies. Finally, a novel 1DCNN architecture with specialized training strategies learns the GLCA system’s behavioral patterns, enabling accurate prediction of criticality for newly added load cases without computationally intensive reanalysis. The results demonstrate strong agreement between GLCA and high-fidelity model analyses: quantitative ranking achieves 95.98% average accuracy with complete identification of critical load cases. Predictions for new load cases yield coefficients of determination (R2) > 0.98 and 97.91% average criticality classification accuracy. Furthermore, GLCA operates 335 times more efficiently than high-fidelity finite element analysis. This approach effectively substitutes high-fidelity modeling during load development, reducing human effort and shortening aircraft design iteration cycles.
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(This article belongs to the Section Aerospace Actuators)
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Open AccessArticle
Fault Diagnosis Method of Micro-Motor Based on Jump Plus AM-FM Mode Decomposition and Symmetrized Dot Pattern
by
Zhengyang Gu, Yufang Bai, Junsong Yu and Junli Chen
Actuators 2025, 14(8), 405; https://doi.org/10.3390/act14080405 - 13 Aug 2025
Abstract
Micro-motors are essential for power drive systems, and efficient fault diagnosis is crucial to reduce safety risks and economic losses caused by failures. However, the fault signals from micro-motors typically exhibit weak and unclear characteristics. To address this challenge, this paper proposes a
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Micro-motors are essential for power drive systems, and efficient fault diagnosis is crucial to reduce safety risks and economic losses caused by failures. However, the fault signals from micro-motors typically exhibit weak and unclear characteristics. To address this challenge, this paper proposes a novel fault diagnosis method that integrates jump plus AM-FM mode decomposition (JMD), symmetrized dot pattern (SDP) visualization, and an improved convolutional neural network (ICNN). Firstly, we employed the jump plus AM-FM mode decomposition technique to decompose the mixed fault signals, addressing the problem of mode mixing in traditional decomposition methods. Then, the intrinsic mode functions (IMFs) decomposed by JMD serve as the multi-channel inputs for symmetrized dot pattern, constructing a two-dimensional polar coordinate petal image. This process achieves both signal reconstruction and visual enhancement of fault features simultaneously. Finally, this paper designed an ICNN method with LeakyReLU activation function to address the vanishing gradient problem and enhance classification accuracy and training efficiency for fault diagnosis. Experimental results indicate that the proposed JMD-SDP-ICNN method outperforms traditional methods with a significantly superior fault classification accuracy of up to 99.2381%. It can offer a potential solution for the monitoring of electromechanical structures under complex conditions.
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(This article belongs to the Section Actuators for Manufacturing Systems)
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Open AccessArticle
Electrochemical Properties and Electromechanical Analysis of a Stacked Polyvinyl Chloride (PVC) Gel Actuator
by
Kinji Asaka, Zicai Zhu and Minoru Hashimoto
Actuators 2025, 14(8), 404; https://doi.org/10.3390/act14080404 - 13 Aug 2025
Abstract
We investigated the electrochemical properties of and conducted an electromechanical analysis on a stacked polyvinyl chloride (PVC) gel actuator, comprising a PVC gel plasticized with dibutyl adipate (DBA) sandwiched between a metal mesh and a metal disk electrode. In this study, we examined
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We investigated the electrochemical properties of and conducted an electromechanical analysis on a stacked polyvinyl chloride (PVC) gel actuator, comprising a PVC gel plasticized with dibutyl adipate (DBA) sandwiched between a metal mesh and a metal disk electrode. In this study, we examined the electrochemical impedance, displacement, and electric current responses to square-wave voltage inputs. The linear motion of PVC gel actuators with and without ionic liquid (IL) additives was analyzed in relation to the mesh size and metal composition of the mesh electrode. The displacement increased with decreasing mesh numbers, indicating that displacement increases with increasing wire diameter and space length. The linear motion of the stacked PVC gel actuators with and without IL additives depended on the metal species of the mesh electrodes. The electrochemical impedance of the stacked PVC gel actuators under DC voltage application was analyzed with and without the IL. Based on the electromechanical and electrochemical results, a deformation model was developed to describe the linear motion of stacked PVC gel actuators in response to the applied voltage. The model attributed this motion to the deformation induced by Maxwell stress in the solvent-rich layer, successfully accounting for the experimental observations.
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(This article belongs to the Special Issue Actuators in 2025)
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Open AccessArticle
A Novel Kinematic Calibration Method for Industrial Robots Based on the Improved Grey Wolf Optimization Algorithm
by
Bingzhang Cao, Jiuwei Yu, Yi Zhang, Peijun Liu, Yifan Zhang, Hongwei Sun, Peng Jin, Jie Lin and Lei Wang
Actuators 2025, 14(8), 403; https://doi.org/10.3390/act14080403 - 13 Aug 2025
Abstract
Due to insufficient absolute positioning accuracy, industrial robots frequently face challenges in efficiently performing drilling and riveting operations during the assembly of aircraft and other large-scale workpieces. To enhance the absolute positioning accuracy of industrial robots, this paper proposes a novel kinematic calibration
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Due to insufficient absolute positioning accuracy, industrial robots frequently face challenges in efficiently performing drilling and riveting operations during the assembly of aircraft and other large-scale workpieces. To enhance the absolute positioning accuracy of industrial robots, this paper proposes a novel kinematic calibration method for industrial robots based on the Improved Grey Wolf Optimization (IGWO) algorithm. Specifically, the method employs an enhanced selection and update strategy to avoid convergence stagnation and local optimum traps. The proposed method features a novel boundary search strategy, which leverages the Dimension-oriented Learning (DL) search strategy to enhance search speed and stability. Through parameter identification and calibration experiments, the effectiveness of the method was validated using an ABB IRB4600 industrial robot and a Leica laser tracker. Additionally, compared with the Levenberg–Marquardt (LM) algorithm, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), the IGWO algorithm demonstrates faster convergence and superior optimization performance. According to the calibration experimental results, by applying the IGWO algorithm, the absolute positioning accuracy of the industrial robot is ultimately improved from 1.918 mm to 0.475 mm and the absolute positioning accuracy is improved by 75.2%.
Full article
(This article belongs to the Special Issue Intelligent Sensing, Control and Actuation in Networked Systems)
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Open AccessArticle
Reliable Neural Network Control for Active Vibration Suppression of Uncertain Structures
by
Jinglei Gong and Xiaojun Wang
Actuators 2025, 14(8), 402; https://doi.org/10.3390/act14080402 - 13 Aug 2025
Abstract
This paper proposes a novel reliable neural network control (NNC) method for active vibration control of uncertain structures. First, reliable model predictive control (MPC) was established by introducing nonprobabilistic reliability constraints into traditional MPC. An importance sampling strategy was established to improve the
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This paper proposes a novel reliable neural network control (NNC) method for active vibration control of uncertain structures. First, reliable model predictive control (MPC) was established by introducing nonprobabilistic reliability constraints into traditional MPC. An importance sampling strategy was established to improve the efficiency of the entire training process to achieve sufficient accuracy. An adaptive nonprobabilistic Kalman filter was further proposed for estimating the uncertain region of system states. Compared to existing reliability-based control methods, the proposed reliable NNC ensured structural safety across broader loads. Compared with reliable MPC, reliable NNC significantly reduced the online computational load, making it suitable for vibration control of high-frequency complex structural systems. The effectiveness and superiority of the proposed reliable NNC were validated through two numerical examples and experimental verification.
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(This article belongs to the Section Control Systems)
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Open AccessArticle
Research on Hierarchical Composite Adaptive Sliding Mode Control for Position and Attitude of Hexarotor UAVs
by
Xiaowei Han, Hai Wang, Nanmu Hui and Gaofeng Yue
Actuators 2025, 14(8), 401; https://doi.org/10.3390/act14080401 - 12 Aug 2025
Abstract
This study proposes a hierarchical composite adaptive sliding-mode control strategy to address the strong nonlinear dynamics of a hexarotor Unmanned Aerial Vehicle (UAV) and the external disturbances encountered during flight. First, within the position-control loop, a Terminal Sliding Mode Control (TSMC) is designed
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This study proposes a hierarchical composite adaptive sliding-mode control strategy to address the strong nonlinear dynamics of a hexarotor Unmanned Aerial Vehicle (UAV) and the external disturbances encountered during flight. First, within the position-control loop, a Terminal Sliding Mode Control (TSMC) is designed to guarantee finite-time convergence of the system states, thereby significantly improving the UAV’s rapid response to complex trajectories. Concurrently, an online Adaptive rates mechanism is introduced to estimate and compensate unknown disturbances and modeling uncertainties in real time, further enhancing disturbance rejection. In the attitude-control loop, a Super-twisting Sliding Mode Control (STSMC) method is employed, where an Adaptive rate law dynamically adjusts the sliding gain to prevent overestimation and high-frequency chattering, while ensuring fast convergence and smooth response. To comprehensively validate the feasibility and superiority of the proposed scheme, a representative helical trajectory-tracking experiment was conducted and systematically compared, via simulation, against conventional control methods. Experimental results demonstrate that the proposed approach achieves stable control within 0.15 s, with maximum position and attitude tracking errors of 0.05 m and 0.15°, respectively. Moreover, it exhibits enhanced robustness and adaptability to external disturbances and parameter uncertainties, effectively improving the motion-control performance of hexacopter UAVs in complex missions.
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(This article belongs to the Section Aerospace Actuators)
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Open AccessArticle
Modeling, Dynamic Characterization, and Performance Analysis of a 2.2 kW BLDC Motor Under Fixed Load Torque Levels and Variable Speed Inputs: An Experimental Study
by
Ayman Ibrahim Abouseda, Resat Doruk, Ali Emin and Ozgur Akdeniz
Actuators 2025, 14(8), 400; https://doi.org/10.3390/act14080400 - 12 Aug 2025
Abstract
Accurate modeling and performance analysis of brushless DC (BLDC) motors are essential for high-efficiency control in modern drive systems. In this article, a BLDC motor was modeled using system identification techniques. In addition, experimental data were collected from the BLDC motor, including its
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Accurate modeling and performance analysis of brushless DC (BLDC) motors are essential for high-efficiency control in modern drive systems. In this article, a BLDC motor was modeled using system identification techniques. In addition, experimental data were collected from the BLDC motor, including its speed response to various input signals. Using system identification tools, particularly those provided by MATLAB/Simulink R2024b, an approximation model of the BLDC motor was constructed to represent the motor’s dynamic behavior. The identified model was experimentally validated using various input signals, demonstrating its accuracy and generalizability under different operating conditions. Additionally, a series of mechanical load tests was conducted using the AVL eddy-current dynamometer to evaluate performance under practical operating conditions. Fixed load torques were applied across a range of motor speeds, and multiple torque levels were tested to assess the motor’s dynamic response. Electrical power, mechanical power, and efficiency of the entire system were computed for each case to assess overall system performance. Moreover, the real-time state of charge (SOC) of Lithium-ion (Li-ion) battery was estimated using the Coulomb counting method to analyze the impact of Li-ion battery energy level on the BLDC motor efficiency. The study offers valuable insights into the motor’s dynamic and energetic behavior, forming a foundation for robust control design and real-time application development.
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(This article belongs to the Special Issue Actuator Technologies and Control: Materials, Devices and Applications)
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Open AccessArticle
Modeling, Fabrication and Control Optimization Based on Fuzzy PID of Multi-Chamber Flexible Mechanisms
by
Pengyun Zhang, Maolin Cai, Geng Wang, Hui Zhang, Qihui Yu, Sikai Lv, Liang Wang, Yeming Zhang and Feng Wei
Actuators 2025, 14(8), 399; https://doi.org/10.3390/act14080399 - 11 Aug 2025
Abstract
This paper focuses on the design and control of multi-chamber flexible mechanisms and explores and controls optimization under pneumatic actuation. Flexible robots, due to the flexibility and adaptability of their materials, demonstrate unique advantages in applications such as underwater operations and precise grasping.
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This paper focuses on the design and control of multi-chamber flexible mechanisms and explores and controls optimization under pneumatic actuation. Flexible robots, due to the flexibility and adaptability of their materials, demonstrate unique advantages in applications such as underwater operations and precise grasping. In this study, a flexible mechanism based on a three-chamber design is proposed, with a prototype fabricated using 3D printing technology. Both simulation and experimental analyses are conducted to delve into its bending characteristics. The Yeoh model is utilized for the simulation analysis of silicone rubber material, revealing the deformation behavior of the mechanism under different pneumatic pressures. A pneumatic control system based on a microcontroller is developed, and a fuzzy PID control algorithm is introduced to enhance the traditional PID, achieving quicker response times and more precise control outcomes. Experimental results demonstrate that the simulations based on the Yeoh model align well with experimental data, and the improved fuzzy PID algorithm exhibits excellent performance in the complex attitude control of the flexible mechanism. The findings provide significant data support for the application of multi-chamber flexible mechanisms and establish a foundation for future design and control optimization endeavors.
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(This article belongs to the Section Actuators for Robotics)
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Open AccessArticle
A Cross-Modal Multi-Layer Feature Fusion Meta-Learning Approach for Fault Diagnosis Under Class-Imbalanced Conditions
by
Haoyu Luo, Mengyu Liu, Zihao Deng, Zhe Cheng, Yi Yang, Guoji Shen, Niaoqing Hu, Hongpeng Xiao and Zhitao Xing
Actuators 2025, 14(8), 398; https://doi.org/10.3390/act14080398 - 11 Aug 2025
Abstract
In practical applications, intelligent diagnostic methods for actuator-integrated gearboxes in industrial driving systems encounter challenges such as the scarcity of fault samples and variable operating conditions, which undermine diagnostic accuracy. This paper introduces a multi-layer feature fusion meta-learning (MLFFML) approach to address fault
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In practical applications, intelligent diagnostic methods for actuator-integrated gearboxes in industrial driving systems encounter challenges such as the scarcity of fault samples and variable operating conditions, which undermine diagnostic accuracy. This paper introduces a multi-layer feature fusion meta-learning (MLFFML) approach to address fault diagnosis problems in cross-condition scenarios with class imbalance. First, meta-training is performed to develop a mature fault diagnosis model on the source domain, obtaining cross-domain meta-knowledge; subsequently, meta-testing is conducted on the target domain, extracting meta-features from limited fault samples and abundant healthy samples to rapidly adjust model parameters. For data augmentation, this paper proposes a frequency-domain weighted mixing (FWM) method that preserves the physical plausibility of signals while enhancing sample diversity. Regarding the feature extractor, this paper integrates shallow and deep features by replacing the first layer of the feature extraction module with a dual-stream wavelet convolution block (DWCB), which transforms actuator vibration or acoustic signals into the time-frequency space to flexibly capture fault characteristics and fuses information from both amplitude and phase aspects; following the convolutional network, an encoder layer of the Transformer network is incorporated, containing multi-head self-attention mechanisms and feedforward neural networks to comprehensively consider dependencies among different channel features, thereby achieving a larger receptive field compared to other methods for actuation system monitoring. Furthermore, this paper experimentally investigates cross-modal scenarios where vibration signals exist in the source domain while only acoustic signals are available in the target domain, specifically validating the approach on industrial actuator assemblies.
Full article
(This article belongs to the Special Issue AI, Designing, Sensing, Instrumentation, Diagnosis, Controlling, and Integration of Actuators in Digital Manufacturing—2nd Edition)
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Open AccessArticle
Hybrid EEG-EMG Control Scheme for Multiple Degrees of Freedom Upper-Limb Prostheses
by
Sorelis Isabel Bandes Rodriguez and Yasuharu Koike
Actuators 2025, 14(8), 397; https://doi.org/10.3390/act14080397 - 11 Aug 2025
Abstract
Upper-limb motor disabilities and amputation pose a significant burden on individuals, hindering their ability to perform daily activities independently. While various research studies aim to enhance the performance of current upper-limb prosthetic devices, electrically activated prostheses still face challenges in achieving optimal functionality.
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Upper-limb motor disabilities and amputation pose a significant burden on individuals, hindering their ability to perform daily activities independently. While various research studies aim to enhance the performance of current upper-limb prosthetic devices, electrically activated prostheses still face challenges in achieving optimal functionality. This paper explores the potential of utilizing electromyogram (EMG) and electroencephalogram (EEG) signals to not only decipher movement across multiple degrees of freedom (DOFs) but also offer a more intuitive means of control. In this study, six distinct control schemes for upper-limb prosthetic devices are proposed, each with different combinations of EEG and EMG signals. These schemes were designed to control multiple degrees-of-freedom movements, encompassing five different hand and forearm actions (hand-open, hand-close, wrist pronation, wrist supination, and rest-state). Using Linear Discriminant Analysis as a model results in classification accuracies of over 85% for combined EEG-EMG control schemes. The results suggest promising advancements in the field and show the potential for a more effective and user-friendly control interface for upper-limb prosthetic devices.
Full article
(This article belongs to the Special Issue Intelligent Systems, Robots and Devices for Healthcare and Rehabilitation)
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Open AccessArticle
Adaptive Robust Stable Tracking Control of Two-Axis Coupled Electromechanical Actuation System Based on Friction Compensation
by
Shusen Yuan, Wenxiang Deng, Wenjun Yi, Jianyong Yao, Guolai Yang and Jun Guan
Actuators 2025, 14(8), 396; https://doi.org/10.3390/act14080396 - 9 Aug 2025
Abstract
The two-axis coupled electromechanical actuation system (TCEAS) is widely utilized in multiple industrial fields, but its tracking performance and stability are severely hampered by complex nonlinear friction, parameter uncertainties, and strong coupling effects. To address these issues, this paper proposes an adaptive robust
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The two-axis coupled electromechanical actuation system (TCEAS) is widely utilized in multiple industrial fields, but its tracking performance and stability are severely hampered by complex nonlinear friction, parameter uncertainties, and strong coupling effects. To address these issues, this paper proposes an adaptive robust stable tracking control (ARSTC) method with friction compensation. First, the friction characteristic of TCEAS is analyzed and a continuously differentiable friction moment function is introduced to accurately describe the nonlinear friction phenomenon. Then, a dynamic analysis model for the system considering friction nonlinearity and model uncertainty is established. Furthermore, the developed ARSTC algorithm leverages adaptive control to estimate and compensate unknown friction parameters (enhancing precision) and robust control to suppress disturbances (ensuring stability). Finally, the superiority is jointly verified by stability analysis and extensive comparative numerical test results. This work demonstrates a practical approach for high-precision control of TCEAS, which has important theoretical significance.
Full article
(This article belongs to the Special Issue Motion Planning, Trajectory Prediction, and Control for Robotics)
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Open AccessArticle
Minimizing Power Losses in BLDC Motor Drives Through Adaptive Flux Control: A Real-Time Experimental Study
by
Mohamed Fadi Kethiri, Omar Charrouf, Achour Betka, Muhammad Salman and Chiara Boccaletti
Actuators 2025, 14(8), 395; https://doi.org/10.3390/act14080395 - 8 Aug 2025
Abstract
This paper presents a novel methodology for minimizing power losses in brushless DC (BLDC) motors through the implementation of adaptive flux control techniques. Conventional motor control strategies, such as direct torque control (DTC), typically employ fixed flux values, which often result in suboptimal
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This paper presents a novel methodology for minimizing power losses in brushless DC (BLDC) motors through the implementation of adaptive flux control techniques. Conventional motor control strategies, such as direct torque control (DTC), typically employ fixed flux values, which often result in suboptimal performance, particularly under dynamic load and speed variations. To mitigate this inherent limitation, two adaptive flux control methods are introduced: incremental conductance (IncCond) and fuzzy logic. These proposed strategies facilitate real-time dynamic adjustment of the stator flux, thereby optimizing motor performance and significantly enhancing system efficiency. Experimental validation confirms the efficacy of these adaptive techniques, demonstrating substantial improvements in power loss reduction and overall efficiency when compared to traditional fixed flux control strategies. Notably, the fuzzy logic control strategy achieves the highest efficiency, registering a system efficiency of %, which surpasses both the incremental conductance method and conventional fixed flux control. These findings underscore the considerable potential of adaptive flux control in applications where energy efficiency is paramount, including electric vehicles and renewable energy-driven systems.
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(This article belongs to the Special Issue Actuator Technologies and Control: Materials, Devices and Applications)
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Open AccessArticle
Autonomous Swing Motion Planning and Control for the Unloading Process of Electric Rope Shovels
by
Yi-Cheng Gao, Zhen-Cai Zhu and Qing-Guo Wang
Actuators 2025, 14(8), 394; https://doi.org/10.3390/act14080394 - 8 Aug 2025
Abstract
Electric rope shovels play a critical role in open-pit mining, where their automation and operational efficiency directly affect productivity. This paper presents a LiDAR-based relative positioning method to determine the spatial relationship between the ERS and mining trucks. The method utilizes dynamic DBSCAN
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Electric rope shovels play a critical role in open-pit mining, where their automation and operational efficiency directly affect productivity. This paper presents a LiDAR-based relative positioning method to determine the spatial relationship between the ERS and mining trucks. The method utilizes dynamic DBSCAN for noise removal and RANSAC for truck edge detection, enabling robust and accurate localization. Leveraging this positioning data, a time-optimal trajectory planning strategy is proposed specifically for autonomous swing motion during the unloading process. The planner incorporates velocity and acceleration constraints to ensure smooth and efficient movement, while obstacle avoidance mechanisms are introduced to enhance safety in constrained excavation environments. To execute the planned trajectory with high precision, a neural network-based sliding-mode controller is designed. An adaptive RBF network is integrated to improve adaptability to model uncertainties and external disturbances. Experimental results on a scaled-down prototype validate the effectiveness of the proposed positioning, planning, and control strategies in enabling accurate and autonomous swing operation for efficient unloading.
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(This article belongs to the Section Control Systems)
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Open AccessArticle
Adaptive Robust Impedance Control of Grinding Robots Based on an RBFNN and the Exponential Reaching Law
by
Lin Jia, Kun Chen, Zeyu Liao, Aodong Qiu and Mingjian Cao
Actuators 2025, 14(8), 393; https://doi.org/10.3390/act14080393 - 8 Aug 2025
Abstract
Given that grinding robots are easily affected by internal and external disturbances when machining complex surfaces with high precision, in this study, an adaptive robust impedance control method combining a radial basis function neural network (RBFNN) and sliding mode control (SMC) is proposed.
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Given that grinding robots are easily affected by internal and external disturbances when machining complex surfaces with high precision, in this study, an adaptive robust impedance control method combining a radial basis function neural network (RBFNN) and sliding mode control (SMC) is proposed. In a Cartesian coordinate system, we first use the universal approximation ability of the RBFNN to accurately identify and actively compensate for complex unknown disturbances in robot dynamics online. Then, an improved sliding mode impedance controller, which uses robust sliding mode control to effectively suppress the influence of RBFNN identification error and residual disturbance on trajectory tracking and ensure the accuracy of impedance control, is implemented. This approach improves the control performance and overcomes the inherent chattering phenomenon of the traditional sliding mode.
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(This article belongs to the Section Actuators for Manufacturing Systems)
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