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Actuators
Special Issues
Advanced Actuation and Control in Intelligent Robots and Autonomous Systems
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Advanced Actuation and Control in Intelligent Robots and Autonomous Systems

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 3396

Editors

Dr. Mohamed Reda

E-Mail Website
Guest Editor
School of Engineering, University of Central Lancashire, Preston PR1 2HE, UK
Interests: steering control; steering angle encoder; driverless pod; Ackermann steering; electric power steering; Harris hawks optimization; CEC2020 benchmark; transient response
Prof. Dr. Ahmed Mahmoud Onsy

E-Mail Website
Guest Editor
School of Engineering, University of Central Lancashire, Preston PR1 2HE, UK
Interests: intelligent maintenance systems; advanced mechatronics; embedded systems; path planning; robotic operating system
Dr. Ali Ghanbari

E-Mail Website
Guest Editor
School of Engineering and Computing, University of Central Lancashire, Preston PR1 2HE, UK
Interests: micro/nanorobotics; control of magnetic microrobots; soft sensors; thin films for biomedical applications; micro/nanofabrication
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of intelligent autonomous systems is rapidly advancing, driven by innovations in actuation, control, and machine intelligence. Modern robotic systems rely on high-performance actuators and adaptive control architectures to achieve precise, reliable, and intelligent behavior. This Special Issue aims to bring together advanced research focused on the design, integration, and optimization of actuation and control mechanisms in autonomous and robotic platforms. Topics of interest include smart actuators, adaptive and fault-tolerant control, optimization-based path planning, and real-time embedded control systems.

Emphasis is placed on practical implementations, simulation frameworks, and experimental validation, especially within applications such as autonomous driving, wearable robotics, assistive systems, and intelligent manufacturing. We especially welcome contributions that apply artificial intelligence (AI), meta-heuristic optimization, or data-driven learning algorithms to address real-world challenges in actuation and control. Both theoretical developments and application-driven studies are encouraged. This issue offers a platform for researchers and engineers to share their advancements in making autonomous systems more responsive, robust, and efficient.

Dr. Mohamed Reda
Prof. Dr. Ahmed Mahmoud Onsy
Dr. Ali Ghanbari
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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 actuation
  • adaptive control systems
  • autonomous robotics
  • meta-heuristic optimization
  • path planning and trajectory control
  • embedded and real-time systems
  • artificial intelligence in control
  • fault-tolerant control
  • bio-inspired robotics
  • autonomous driving systems

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

Published Papers (3 papers)

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Research

32 pages, 6605 KB  
Article
A Hybrid Enhanced Harris Hawks Optimization Algorithm for AGV Path Planning in Smart Warehousing
by Guiqiang Cheng, Chunfang Li, Yuhang Ren, Jiankun Li, Yuqi Yao, Yiwen Zhang, Linsen Song, Xinming Zhang, Jingru Liu, Lei Gong and Zhenglei Yu
Actuators 2026, 15(6), 294; https://doi.org/10.3390/act15060294 - 27 May 2026
Viewed by 344
Abstract
Automated Guided Vehicles (AGVs) play a crucial role in intelligent warehousing; however, effective path planning remains challenging because of obstacles, safety constraints, and the risk of suboptimal routes. This study proposes an improved Harris Hawks Optimization algorithm for AGV path planning, introducing strategies [...] Read more.
Automated Guided Vehicles (AGVs) play a crucial role in intelligent warehousing; however, effective path planning remains challenging because of obstacles, safety constraints, and the risk of suboptimal routes. This study proposes an improved Harris Hawks Optimization algorithm for AGV path planning, introducing strategies to enhance initial solution quality, balance global and local search, and avoid local optima. The proposed algorithm generates shorter, smoother, and safer paths, as demonstrated through benchmark tests, multi-scale grid-map simulations, and real-world AGV experiments. In terms of path length and computational efficiency, the enhanced algorithm significantly outperforms the original HHO, reducing average path length by 10.81% and average travel time by 11.94%. These results demonstrate that the proposed method provides a practical and reliable solution for autonomous warehouse navigation and significantly improves AGV path-planning performance. Full article
(This article belongs to the Special Issue Advanced Actuation and Control in Intelligent Robots and Autonomous Systems)
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17 pages, 912 KB  
Article
Adaptive Actor–Critic Optimal Tracking Control for a Class of High-Order Nonlinear Systems with Partially Unknown Dynamics
by Dengguo Xu, Xinsuo Li, Fapeng Li and Jingbei Tian
Actuators 2026, 15(3), 138; https://doi.org/10.3390/act15030138 - 2 Mar 2026
Cited by 1 | Viewed by 562
Abstract
Optimal tracking control for high-order partially unknown nonlinear systems poses significant challenges, particularly in deriving tractable solutions without requiring persistent excitation (PE) conditions or precise system models. This study develops an adaptive optimal tracking control law using neural network (NN)-based reinforcement learning (RL) [...] Read more.
Optimal tracking control for high-order partially unknown nonlinear systems poses significant challenges, particularly in deriving tractable solutions without requiring persistent excitation (PE) conditions or precise system models. This study develops an adaptive optimal tracking control law using neural network (NN)-based reinforcement learning (RL) for high-order partially unknown nonlinear systems. By designing a cost function associated with the sliding mode variable (SMV), the original tracking control problem is equivalently transformed into solving the optimal control problem related to the tracking Hamilton–Jacobi–Bellman (HJB) equation. Since the analytical solution of the HJB equation is generally intractable, we employ a policy iteration algorithm derived from the HJB equation, where both the partial derivative of the optimal tracking cost function and the optimal control law are approximated by NNs. The proposed RL framework achieves simplification through actor–critic training laws derived under the condition that a simple function is zero. Finally, both a numerical example and a single-link robotic arm application are provided to demonstrate the effectiveness and advantages of the proposed adaptive optimal tracking control method. Full article
(This article belongs to the Special Issue Advanced Actuation and Control in Intelligent Robots and Autonomous Systems)
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18 pages, 4034 KB  
Article
Analysis of Time Drift and Real-Time Challenges in Programmable Logic Controller-Based Industrial Automation Systems: Insights from 24-Hour and 14-Day Tests
by Ayah Hijazi, Mátyás Andó and Zoltán Pödör
Actuators 2025, 14(11), 524; https://doi.org/10.3390/act14110524 - 28 Oct 2025
Cited by 1 | Viewed by 1928
Abstract
Ensuring the reliability and temporal accuracy of real-time data transmission in industrial systems presents significant challenges. This study evaluates the performance of a Siemens Programmable Logic Controller (PLC) transmitting data to a MongoDB database via Node-RED over 24 h and 14-day intervals. Key [...] Read more.
Ensuring the reliability and temporal accuracy of real-time data transmission in industrial systems presents significant challenges. This study evaluates the performance of a Siemens Programmable Logic Controller (PLC) transmitting data to a MongoDB database via Node-RED over 24 h and 14-day intervals. Key issues observed include time drift, timestamp misalignment, and forward/backward time jumps, mainly resulting from Node-RED’s internal timing adjustments. These anomalies compromised the integrity of time-sensitive data. A significant disruption on day 8 due to a power outage introduced data gaps and required manual system recovery. Additional spikes in missing data were observed after day 12. The Predictive Missing Value (PMV) model addressed these gaps. The model achieved strong accuracy at larger intervals (e.g., 5 min) but showed reduced performance at finer resolutions (1–2 min) due to the irregularity of data patterns. This research highlights the difficulty of maintaining temporal consistency in long-term, real-time systems. It also evaluates the PMV model’s effectiveness in mitigating data loss while acknowledging its limitations under complex timing disruptions. Full article
(This article belongs to the Special Issue Advanced Actuation and Control in Intelligent Robots and Autonomous Systems)
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