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Actuators
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Advances in Intelligent Control of Actuator Systems
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Advances in Intelligent Control of Actuator Systems

A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Control Systems".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 3473

Special Issue Editors

Dr. Kun Zhang

E-Mail Website
Guest Editor
Department of Aerospace Intelligent Science and Technology, School of Astronautics, Beihang University, Beijing 100191,China.
Interests: intelligent controls; learning control; adaptive dynamic programming; data-driven control; vehicles
Dr. Guoqiang Tan

E-Mail Website
Guest Editor
Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3TU, UK
Interests: neural networks; state estimation; optimization; intelligent control; dual control; autonomous vehicles and robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Ting Wang

E-Mail Website
Guest Editor
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
Interests: control theory; errors; feedback control; binary-valued measurement; communications channels; consensus; consensus problems; control strategies; convergence; estimation errors; event-triggered controls; projection algorithms; random noise; multi agent systems

Special Issue Information

Dear Colleagues,

Actuator systems are systems composed of actuators or mechanisms that convert energy (typically electric, pneumatic, mechanical, or hydraulic) into physical motion or mechanical work to perform specific control tasks. Actuator systems have impacts in many areas, such as energy, robotics, manufacturing, transportation, and aerospace. They bridge the gap between control systems and the practical applications, enabling dynamic and automated interactions with the environment. With the development of technology, the demand of advances in intelligent control has been continuously increasing. This Special Issue aims to provide a forum for researchers and developers to exchange ideas, discuss recent trends, and share achieved results related to these fields. Original and innovative research works from both academia and industry are welcome. Potential topics of interest include, but are not limited to, the following:

  • The intelligent control of actuator systems and related applications;
  • Secure or safety-related techniques for actuator systems;
  • Machine learning and computational intelligence for actuator systems;
  • The dynamic optimization of actuator systems and related applications;
  • Advanced methodology for actuator system operation and control;
  • Data-driven techniques for the identification, modeling, and control of actuator systems
  • Smart planning, market design, and regulatory frameworks for actuator systems;
  • The integration of other emerging technologies in the operation, control, optimization, and planning of actuator systems.

Dr. Kun Zhang
Dr. Guoqiang Tan
Dr. Ting Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Actuators is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent control
  • modeling and analysis
  • control applications
  • actuator systems
  • sensing nonlinear system

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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19 pages, 4016 KB  
Article
Multibody Dynamics Simulation of Upper Extremity Rehabilitation Exoskeleton During Task-Oriented Exercises
by Piotr Falkowski and Krzysztof Zawalski
Actuators 2025, 14(9), 426; https://doi.org/10.3390/act14090426 - 30 Aug 2025
Viewed by 666
Abstract
Population aging intensifies the demand for rehabilitation services, which are already suffering from staff shortages. In response to this challenge, the implementation of new technologies in physiotherapy is needed. For such a task, rehabilitation exoskeletons can be used. While designing such tools, their [...] Read more.
Population aging intensifies the demand for rehabilitation services, which are already suffering from staff shortages. In response to this challenge, the implementation of new technologies in physiotherapy is needed. For such a task, rehabilitation exoskeletons can be used. While designing such tools, their functionality and safety must be ensured. Therefore, simulations of their strength and kinematics must meet set criteria. This paper aims to present a methodology for simulating the dynamics of rehabilitation exoskeletons during activities of daily living and determining the reactions in the construction’s joints, as well as the required driving torques. The methodology is applied to the SmartEx-Home exoskeleton. Two versions of a multibody model were developed in the Matlab/Simulink environment—a rigid-only version and one with deformable components. The kinematic chain of construction was reflected with the driven rotational joints and modeled passive sliding open bearings. The simulation outputs include the driving torques and joint reaction forces and the torques for various input trajectories registered using IMU sensors on human participants. The results obtained in the investigation show that in general, to mobilize shoulder flexion/extension or abduction/adduction, around 30 Nm of torque is required in such a lightweight exoskeleton. For elbow flexion/extension, around 10 Nm of torque is needed. All of the reactions are presented in tables for all of the characteristic points on the passive and active joints, as well as the attachments of the extremities. This methodology provides realistic load estimations and can be universally used for similar structures. The presented numerical results can be used as the basis for a strength analysis and motor or force sensor selection. They will be directly implemented for the process of mass minimization of the SmartEx-Home exoskeleton based on computational optimization. Full article
(This article belongs to the Special Issue Advances in Intelligent Control of Actuator Systems)
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18 pages, 738 KB  
Article
PD-like Consensus Tracking Algorithm for Discrete Multi-Agent Systems with Time-Varying Reference State Under Binary-Valued Communication
by Yuqi Wu, Xu Sun, Ting Wang and Jie Wang
Actuators 2025, 14(6), 267; https://doi.org/10.3390/act14060267 - 28 May 2025
Cited by 1 | Viewed by 539
Abstract
In this paper, a new consensus tracking control algorithm is proposed for discrete multi-agent systems under binary communication with noise and a time-varying reference state. Unlike previous studies, the leader’s reference state is time-varying and convergent. Each agent estimates its neighbors’ states using [...] Read more.
In this paper, a new consensus tracking control algorithm is proposed for discrete multi-agent systems under binary communication with noise and a time-varying reference state. Unlike previous studies, the leader’s reference state is time-varying and convergent. Each agent estimates its neighbors’ states using a recursive projection algorithm based on noisy binary-valued information. The controller design incorporates both the error between the current and estimated states and the rate of change of the estimated state, resulting in a proportional–derivative-like algorithm (PD-like algorithm). The algorithm achieves consensus tracking with a convergence rate of O(1/tε) under certain conditions. Finally, numerical simulations demonstrate the algorithm’s effectiveness and validate the theoretical results. Full article
(This article belongs to the Special Issue Advances in Intelligent Control of Actuator Systems)
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19 pages, 4968 KB  
Article
Event-Triggered Control for Flapping-Wing Robot Aircraft System Based on High-Gain Observers
by Chenxu Xiao, Li Tang, Fei Wang, Sheng You, Hao Xu, Mingchuang Chen and Zhiyuan Lu
Actuators 2025, 14(4), 190; https://doi.org/10.3390/act14040190 - 13 Apr 2025
Cited by 1 | Viewed by 534
Abstract
In this paper, an event-triggered (ET) control strategy for a flapping-wing robot aircraft system (FWRA) based on high-gain observers is investigated. To solve the vibration problems of bending deformation and torsional deformation that may be encountered in an FWRA during flight, a novel [...] Read more.
In this paper, an event-triggered (ET) control strategy for a flapping-wing robot aircraft system (FWRA) based on high-gain observers is investigated. To solve the vibration problems of bending deformation and torsional deformation that may be encountered in an FWRA during flight, a novel control method is proposed. Firstly, high-gain observers are used to accurately estimate the unmeasured states of the system, and then output feedback ET controllers are designed by combining ET mechanisms. These controllers can effectively suppress the vibrations and ensure the stability of the system, and the occurrence of the Zeno phenomenon is effectively prevented, while the communication burden is reduced. Finally, the simulation results verify the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Advances in Intelligent Control of Actuator Systems)
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21 pages, 1226 KB  
Article
RSS Tracking Control for AVs Under Bayesian-Network-Based Intelligent Learning Scheme
by Kun Zhang, Kezhen Han and Nanbin Zhao
Actuators 2025, 14(1), 37; https://doi.org/10.3390/act14010037 - 17 Jan 2025
Cited by 1 | Viewed by 997
Abstract
In complex real-world traffic environments, the task of automatic lane changing becomes extremely challenging for vehicle control systems. Traditional control methods often lack the flexibility and intelligence to accurately capture and respond to dynamic changes in traffic flow. Therefore, developing intelligent control strategies [...] Read more.
In complex real-world traffic environments, the task of automatic lane changing becomes extremely challenging for vehicle control systems. Traditional control methods often lack the flexibility and intelligence to accurately capture and respond to dynamic changes in traffic flow. Therefore, developing intelligent control strategies that can accurately predict the behavior of surrounding vehicles and make corresponding adjustments is crucial. This paper presents an intelligent driving control scheme for autonomous vehicles (AVs) based on a responsibility-sensitive safety (RSS) tracking control mechanism within a Bayesian network intelligent learning framework. Initially, the Bayesian evidence construction method for vehicle lane changing scenarios is studied. Using this method, prior probability tables for lane-hanging vehicles are constructed, and the Bayesian formula is applied to predict the lane changing probabilities of surrounding vehicles. Subsequently, an optimal control method is employed to integrate Bayesian lane changing probabilities into the design of performance indices and auxiliary systems, transforming tracking and safety avoidance tasks into an optimization control problem. Additionally, a critic learning optimal control algorithm is developed to determine the control law. Finally, the proposed tracking control scheme is validated through simulations, demonstrating its reliability and effectiveness. Full article
(This article belongs to the Special Issue Advances in Intelligent Control of Actuator Systems)
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