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Actuators
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Advanced Learning and Intelligent Control Algorithms for Robots
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Advanced Learning and Intelligent Control Algorithms for Robots

A Special Issue of Actuators (ISSN 2076-0825) belonging to the section "Control Systems".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 10730

Editors

Prof. Dr. Haibo Du

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
Interests: nonlinear control; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Lanlin Yu

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
Interests: modelling and cooperative control for complex network systems
Dr. Xiaodong Cheng

E-Mail Website
Guest Editor
Mathematical and Statistical Methods Group (Biometris), Wageningen University & Research, Wageningen, The Netherlands
Interests: systems control and machine learning; specifically including data-driven modeling (building predictive models using system identification and deep learning approaches); model reduction (simplifying models of complex dynamic systems for prediction and control); optimization for control (designing control strategies using optimal control and reinforcement learning)

Special Issue Information

Dear Colleagues,

Robots are now widely used in various sectors and applications. They have become an integral part of modern automation, performing essential functions in industrial automation, logistics, healthcare, transportation, search and rescue, environmental monitoring, and exploration. This Special Issue focuses on exploring the profound theories and diverse applications of advanced learning and intelligent control algorithms for robots. It aims to bring together scholars and professionals from academia and industry to share their recent discoveries, techniques, and practical uses that drive the development of this field.

This Special Issue welcomes original submissions covering various aspects of advanced learning and intelligent control algorithms for robots. We are interested in original works that explore the theoretical foundations, practical implementations, and real-world deployments in, but not limited to, the following areas:

  • Advanced learning techniques for robots;
  • Intelligent control algorithms for robots;
  • Multi-robot cooperation and control systems;
  • Autonomous navigation and control;
  • Decision-making in robotic control systems;
  • Learning and adaptation in robotic control;
  • Swarm robotics control (coordinating collective behaviors, utilizing swarm intelligence in control, and applying swarm robotics with a focus on control);
  • Applications of advanced learning and intelligent control algorithms in robots;
  • Robotic-based motion control simulators;
  • Emerging technologies in robotic control.

We look forward to receiving your contributions and creating an outstanding collection of papers that will shape the future of advanced learning and intelligent control algorithms for robots.

Prof. Dr. Haibo Du
Dr. Lanlin Yu
Dr. Xiaodong Cheng
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

  • robotics
  • advanced learning
  • intelligent control algorithms
  • multi-robot control
  • autonomous navigation control
  • decision-making
  • swarm robotics control

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

Published Papers (7 papers)

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Research

Jump to: Review

17 pages, 3892 KB  
Article
A Novel Bidirectional Beetle-Informed RRT* Connect Path Planning Algorithm for Angle-Steel Tower Operation Robots
by Yansheng Liu, Lanlin Yu, Duochen Bao, Chao Lu and Haibo Du
Actuators 2026, 15(6), 285; https://doi.org/10.3390/act15060285 - 25 May 2026
Viewed by 322
Abstract
This paper proposes a novel Bidirectional Beetle-Informed RRT* (BBI-RRT*) Connect algorithm to enhance the safety and path planning efficiency of 6-DOF robotic manipulators operating in the complex high-altitude environment of angle-steel towers. By digitally reconstructing the tower environment through model registration, the algorithm [...] Read more.
This paper proposes a novel Bidirectional Beetle-Informed RRT* (BBI-RRT*) Connect algorithm to enhance the safety and path planning efficiency of 6-DOF robotic manipulators operating in the complex high-altitude environment of angle-steel towers. By digitally reconstructing the tower environment through model registration, the algorithm establishes an accurate foundation for subsequent path planning. A bidirectional beetle antennae search mechanism is employed to guide node sampling, effectively accelerating the convergence rate of the algorithm. To ensure the generation of feasible path, a multi-constraint objective function is designed to balance path length, smoothness, and operability. Additionally, an Informed RRT* process is integrated to refine the path within an adaptive 3D ellipsoid, achieving global path optimization. Both simulation tests on the Unity platform and real-world experiments are conducted to validate the effectiveness and superiority of the proposed algorithm. Full article
(This article belongs to the Special Issue Advanced Learning and Intelligent Control Algorithms for Robots)
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13 pages, 22772 KB  
Article
Vision Inertial Stabilized Platform-Based Finite-Time Target Tracking Control for Multi-Rotor UAVs
by Jing Zhang, Zhiyong Yang, Wenwu Zhu and Jian Xiao
Actuators 2026, 15(5), 261; https://doi.org/10.3390/act15050261 - 2 May 2026
Cited by 1 | Viewed by 441
Abstract
This paper proposes a finite-time target tracking control for multi-rotor unmanned aerial vehicles (UAVs) based on a vision-inertial-stabilized platform. To address the challenge of stable and accurate moving target tracking, the sliding mode control (SMC) technique is used to overcome limitations of conventional [...] Read more.
This paper proposes a finite-time target tracking control for multi-rotor unmanned aerial vehicles (UAVs) based on a vision-inertial-stabilized platform. To address the challenge of stable and accurate moving target tracking, the sliding mode control (SMC) technique is used to overcome limitations of conventional control algorithms, such as poor robustness and slow convergence speed. First, by computing the pixel deviation between the target and the image center, a kinematic model of the tracking target is established. Then, by introducing homogeneous system theory into the sliding mode surface design, a non-singular fast integral terminal sliding mode control (NFITSMC) is designed for target tracking via regulating the rotational angular acceleration of dual actuators in the vision inertial stabilized platform, thereby driving the pixel deviation to converge to zero in a finite time. Strict theoretical analysis is given to prove the finite-time stability and robustness of the closed-loop control system. Furthermore, simulation results demonstrate that the proposed method maintains higher tracking accuracy than SMC, ISMC, and TSMC. Full article
(This article belongs to the Special Issue Advanced Learning and Intelligent Control Algorithms for Robots)
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32 pages, 32199 KB  
Article
Autonomous Robotic Platform for Precision Viticulture: Integrated Mobility, Multimodal Sensing, and AI-Based Leaf Sampling
by Miriana Russo, Corrado Santoro, Federico Fausto Santoro and Alessio Tudisco
Actuators 2026, 15(2), 91; https://doi.org/10.3390/act15020091 - 2 Feb 2026
Cited by 2 | Viewed by 1560
Abstract
Viticulture is facing growing economic and environmental pressures that demand a transition toward intelligent and autonomous crop management systems. Phytopathologies remain one of the most critical threats, causing substantial yield losses and reducing grape quality, while regulatory restrictions on agrochemicals and sustainability goals [...] Read more.
Viticulture is facing growing economic and environmental pressures that demand a transition toward intelligent and autonomous crop management systems. Phytopathologies remain one of the most critical threats, causing substantial yield losses and reducing grape quality, while regulatory restrictions on agrochemicals and sustainability goals are driving the development of precision agriculture solutions. In this context, early disease detection is crucial; however, current visual inspection methods are hindered by subjectivity, cost, and delayed symptom recognition. This study presents a fully autonomous robotic platform developed within the Agrimet project, enabling continuous, high-frequency monitoring in vineyard environments. The system integrates a tracked mobility base, multimodal sensing using RGB-D and thermal cameras, an AI-based perception framework for leaf localisation, and a compliant six-axis manipulator for biological sampling. A custom control architecture bridges standard autopilot PWM signals with industrial CANopen motor drivers, achieving seamless coordination among all subsystems. Field validation in a Sicilian vineyard demonstrated the platform’s capability to navigate autonomously, acquire multimodal data, and perform precise georeferenced sampling under unstructured conditions. The results confirm the feasibility of holistic robotic systems as a key enabler for sustainable, data-driven viticulture and early disease management. The YOLOv10s detection model achieved good precision and F1-score for leaf detection, while the integrated Kalman filtering visual servoing system demonstrated low spatial tolerance under field conditions despite foliage sway and vibrations. Full article
(This article belongs to the Special Issue Advanced Learning and Intelligent Control Algorithms for Robots)
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22 pages, 2422 KB  
Article
Data-Driven Forward Kinematics for Robotic Spatial Augmented Reality: A Deep Learning Framework Using LSTM and Attention
by Sooyoung Jang, Hanul Yum and Ahyun Lee
Actuators 2025, 14(12), 569; https://doi.org/10.3390/act14120569 - 25 Nov 2025
Cited by 1 | Viewed by 828
Abstract
Robotic Spatial Augmented Reality (RSAR) systems present a unique control challenge as their end-effector is a projection, whose final position depends on both the actuator’s pose and the external environment’s geometry. Accurately controlling this projection first requires predicting the 6-DOF pose of a [...] Read more.
Robotic Spatial Augmented Reality (RSAR) systems present a unique control challenge as their end-effector is a projection, whose final position depends on both the actuator’s pose and the external environment’s geometry. Accurately controlling this projection first requires predicting the 6-DOF pose of a projector-camera unit from joint angles; however, loose kinematic specifications in many RSAR setups make precise analytical models unavailable for this task. This study proposes a novel deep learning model combining Long Short-Term Memory (LSTM) and an Attention Mechanism (LSTM–Attention) to accurately estimate the forward kinematics of a 2-axis Pan-Tilt actuator. To ensure a fair evaluation of intrinsic model performance, a simulation framework using Unity and unified robot description format was developed to generate a noise-free benchmark dataset. The proposed model utilizes a multi-task learning architecture with a geodesic distance loss function to optimize 3-dimensional position and 4-dimensional quaternion rotation separately. Quantitative results show that the proposed LSTM–Attention model achieved the lowest errors (Position MAE: 18.00 mm; Rotation MAE: 3.723 deg), consistently outperforming baseline models like Random Forest by 9.5% and 17.6%, respectively. Qualitative analysis further confirmed its superior stability and outlier suppression. The proposed LSTM–Attention architecture proves to be a effective and accurate methodology for modeling the complex non-linear kinematics of RSAR systems. Full article
(This article belongs to the Special Issue Advanced Learning and Intelligent Control Algorithms for Robots)
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12 pages, 3174 KB  
Article
Modeling and Control for an Aerial Work Quadrotor with a Robotic Arm
by Wenwu Zhu, Fanzeng Wu, Haibo Du, Lei Li and Yao Zhang
Actuators 2025, 14(7), 357; https://doi.org/10.3390/act14070357 - 21 Jul 2025
Viewed by 2385
Abstract
This paper focuses on the integrated modeling and disturbance rejection of the aerial work quadrotor with a robotic arm. First, to address the issues of model incompleteness and parameter uncertainty commonly encountered in traditional Newton–Euler-based modeling approaches for such a system, the Lagrangian [...] Read more.
This paper focuses on the integrated modeling and disturbance rejection of the aerial work quadrotor with a robotic arm. First, to address the issues of model incompleteness and parameter uncertainty commonly encountered in traditional Newton–Euler-based modeling approaches for such a system, the Lagrangian energy conservation principle is adopted. By treating the quadrotor and robotic arm as a unified system, an integrated dynamic model is developed, which accurately captures the coupled dynamics between the aerial platform and the manipulator. The innovative approach fills the gap in existing research where model expressions are incomplete and parameters are ambiguous. Next, to reduce the adverse effects of the robotic arm’s motion on the entire system stability, a finite-time disturbance observer and a fast non-singular terminal sliding mode controller (FNTSMC) are designed. Lyapunov theory is used to prove the finite-time stability of the closed-loop system. It breaks through the limitations of the traditional Lipschitz framework and, for the first time at both the theoretical and methodological levels, achieves finite-time convergence control for the aerial work quadrotor with a robotic arm system. Finally, comparative simulations with the integral sliding mode controller (ISMC), sliding mode controller (SMC), and PID controller demonstrate that the proposed algorithm reduces the regulation time by more than 45% compared to ISMC and SMC, and decreases the overshoot by at least 68% compared to the PID controller, which improves the convergence performance and disturbance rejection capability of the closed-loop system. Full article
(This article belongs to the Special Issue Advanced Learning and Intelligent Control Algorithms for Robots)
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19 pages, 7587 KB  
Article
Three-Vector-Based Smart Model Predictive Torque Control of Surface-Mounted Permanent Magnet Synchronous Motor Drives for Robotic System Based on Genetic Algorithm
by Shenghui Li, Li Ma, Jingrui Hou, Yiqing Ma and Rongbo Lai
Actuators 2025, 14(3), 149; https://doi.org/10.3390/act14030149 - 17 Mar 2025
Cited by 3 | Viewed by 1634
Abstract
Owing to their high performance and high-efficiency controllability, surface-mounted permanent magnet synchronous motors (SPMSMs) have been widely considered for various robotic systems. The conventional three-vector-based model predictive torque control (MPTC) is frequently applied to SPMSMs, while the adjustment of weight factors is difficult. [...] Read more.
Owing to their high performance and high-efficiency controllability, surface-mounted permanent magnet synchronous motors (SPMSMs) have been widely considered for various robotic systems. The conventional three-vector-based model predictive torque control (MPTC) is frequently applied to SPMSMs, while the adjustment of weight factors is difficult. Compared with the five-segment sequence output method, the three-segment sequence output method can effectively reduce the switching frequency. However, the three-segment sequence output method leads to large torque and stator flux ripple. For these issues, a three-vector-based smart MPTC method based on the optimal vector sequence optimized by a genetic algorithm is proposed. Firstly, the reference voltage vector output from the discrete-time sliding mode (DTSM) current controller is utilized to simplify the process of selecting the vectors, and it can enhance the robustness of the SPMSM system. Secondly, an improved cost function is employed to select the optimal vector sequence, aiming to minimize torque and flux ripple. Furthermore, the multi-objective genetic algorithm is leveraged to seek the Pareto solution for weight factors. As a final step, the efficacy of the designed MPTC approach is confirmed through simulations and experiments. Full article
(This article belongs to the Special Issue Advanced Learning and Intelligent Control Algorithms for Robots)
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Review

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47 pages, 21577 KB  
Review
Modern Control Meets Machine Learning: A Review and Taxonomy of Synergistic Approaches for Robotics Applications
by Xiangyu Zhang, Guowei Li, Shahab Shokouhi and May-Win L. Thein
Actuators 2026, 15(5), 235; https://doi.org/10.3390/act15050235 - 27 Apr 2026
Viewed by 2589
Abstract
This paper explores the emerging synergy between control theory and machine learning in robotics, focusing on methods that combine model-based strategies with data-driven adaptation. The authors highlight how classical techniques, such as model predictive control and adaptive control, are being enhanced by reinforcement [...] Read more.
This paper explores the emerging synergy between control theory and machine learning in robotics, focusing on methods that combine model-based strategies with data-driven adaptation. The authors highlight how classical techniques, such as model predictive control and adaptive control, are being enhanced by reinforcement learning, imitation learning, and neural models to address challenges in complex, uncertain environments. Emphasis is placed on real-world platforms (e.g., legged systems, aerial robots, and manipulators) with special attention to advanced domains such as multi-agent systems and coordination. The authors, in addition, establish a taxonomy to categorize these hybrid approaches as “learning-for-control”, “control-for-learning”, or “co-designed architectures”. This paper also reflects upon key open problems, including sim-to-real transfer, safety, and the need for verifiable learning-based controllers, all facets that help to outline a roadmap for future research. Full article
(This article belongs to the Special Issue Advanced Learning and Intelligent Control Algorithms for Robots)
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