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Actuators
Special Issues
Motion Planning, Trajectory Prediction, and Control for Robotics
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Motion Planning, Trajectory Prediction, and Control for Robotics

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1394

Special Issue Editors

Dr. Mingxing Yuan

E-Mail Website
Guest Editor
Institute of Robotics and Automatic Information System, Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin, China
Interests: motion planning and control; mobile robots; teleoperated robots
Special Issues, Collections and Topics in MDPI journals
Dr. Wenxiang Deng

E-Mail Website
Guest Editor
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, China
Interests: nonlinear control theory and applications; hydraulic systems; robotics; adaptive control; state and disturbance observer; nonlinear compensation control
Special Issues, Collections and Topics in MDPI journals
Dr. Yuling Li

E-Mail Website
Guest Editor
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China
Interests: networked robots; teleoperation; human-robot interaction; safety-critical control of robots

Special Issue Information

Dear Colleagues,

Motion planning and control are fundamental techniques for intelligent robotic systems, thus becoming hotspots in the robotics field. Practical robotic systems, including manipulators and ground/aerial/underwater/legged mobile robots, are activated by a variety of actuators such as electrical motors and electro-hydraulic units. These actuators, in practice, are subject to various modeling uncertainties and a number of physical constraints including kinematic and dynamic constraints, which pose significant challenges on the motion planning and control of a robotic system. Therefore, it is crucial to develop advanced motion planning and control strategies considering these modeling uncertainties and physical constraints, motivating this Special Issue.

This Special Issue aims to invite relevant robotic researchers to share their most recent achievements in motion planning, trajectory prediction, and control for robotic systems. Original papers reporting novel approaches and significant results are welcome.

This Special Issue focuses on topics including but not limited to the following:

collision-avoidance path planning; optimal trajectory planning; trajectory prediction in human/environment–robot interactions; system modeling and identification; advanced robotic motion/force control; and human–robot interaction control.

Dr. Mingxing Yuan
Dr. Wenxiang Deng
Dr. Yuling Li
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

  • motion planning
  • motion/force control
  • human–robot interaction
  • trajectory prediction

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

Published Papers (4 papers)

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Research

17 pages, 16780 KiB  
Article
Robust Time-Optimal Kinematic Control of Robotic Manipulators Based on Recurrent Neural Network Against Harmonic Noises
by Yiqun Kuang, Shuai Li and Zhan Li
Actuators 2025, 14(5), 213; https://doi.org/10.3390/act14050213 - 25 Apr 2025
Abstract
Industrial and service manipulators demand implementing time-optimal kinematic control to minimize task duration in a manner of maximizing end-effector velocity during path tracking. However, achieving this objective in the presence of harmonic noise while strictly enforcing joint motion constraints remains a significant challenge. [...] Read more.
Industrial and service manipulators demand implementing time-optimal kinematic control to minimize task duration in a manner of maximizing end-effector velocity during path tracking. However, achieving this objective in the presence of harmonic noise while strictly enforcing joint motion constraints remains a significant challenge. This paper introduces a novel approach that leverages dynamic recurrent neural networks (RNNs) within a constrained optimization framework to deliver robust, time-optimal kinematic control even under harmonic disturbances. We provide a thorough theoretical analysis of the RNN-based control solver, establishing its convergence and optimality. Importantly, our method maximizes end-effector speed without violating any joint velocity limits, thereby enhancing the path-tracking speed compared to previous schemes. Simulation results and physical experiments further demonstrate the effectiveness and superiority of the proposed approach. Full article
(This article belongs to the Special Issue Motion Planning, Trajectory Prediction, and Control for Robotics)
25 pages, 15259 KiB  
Article
Backstepping Command Filter Control for Electromechanical Servo Systems with Unknown Dynamics Based on Reinforcement Learning
by Chenchen Xu, Jian Hu, Jiong Wang, Wenxiang Deng, Jianyong Yao and Xiaoli Zhao
Actuators 2025, 14(3), 155; https://doi.org/10.3390/act14030155 - 19 Mar 2025
Viewed by 176
Abstract
To address the challenges of acquiring precise dynamic models for electromechanical servo systems and the susceptibility of system state information to noise, a backstepping command filter controller based on reinforcement learning is proposed. This method can achieve high-precision and low-energy control in electromechanical [...] Read more.
To address the challenges of acquiring precise dynamic models for electromechanical servo systems and the susceptibility of system state information to noise, a backstepping command filter controller based on reinforcement learning is proposed. This method can achieve high-precision and low-energy control in electromechanical servo systems subject to noise interference and unmodeled disturbances. The proposed method employs a command filter to obtain differential estimations of known signals and process noise. Reinforcement learning is employed to estimate unknown system disturbances, including unmodeled friction and external interference. The weights of the networks are updated using an online gradient descent algorithm, eliminating the need for an offline learning phase. In addition, the Lyapunov function analysis method is used to demonstrate the stability of the closed-loop system. To validate the effectiveness of the proposed method, comparative experiments were conducted using an electromechanical servo experimental platform. The experimental results indicate that, compared to other mainstream controllers, the control algorithm excels in tracking accuracy, response speed, and energy efficiency, and demonstrates significant robustness against system uncertainties and external disturbances. Full article
(This article belongs to the Special Issue Motion Planning, Trajectory Prediction, and Control for Robotics)
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24 pages, 2621 KiB  
Article
Nonlinear Robust Control for Missile Unsupported Random Launch Based on Dynamic Surface and Time Delay Estimation
by Xiaochuan Yu, Hui Sun, Haoyang Liu, Xianglong Liang, Xiaowei Yang and Jianyong Yao
Actuators 2025, 14(3), 142; https://doi.org/10.3390/act14030142 - 13 Mar 2025
Viewed by 261
Abstract
Due to the difficulty in ensuring launch safety under unfavorable launch site conditions, restrictions regarding the selection of launch sites significantly weaken the maneuverability of the unsupported random vertical launch (URVL) mode. In this paper, a nonlinear robust control strategy is proposed to [...] Read more.
Due to the difficulty in ensuring launch safety under unfavorable launch site conditions, restrictions regarding the selection of launch sites significantly weaken the maneuverability of the unsupported random vertical launch (URVL) mode. In this paper, a nonlinear robust control strategy is proposed to control the missile attitude by actively adjusting the oscillation of the launcher through the hydraulic actuator, enhancing the launching safety and the adaptability of the VMLS to the launching site. Firstly, considering the interaction among the launch canister, adapters, and missile, a 6-DOF dynamic model of the launch system is established, in combination with the dynamics of the hydraulic actuator. Then, in order to facilitate the nonlinear controller design, the seventh-order state-space equation is constructed, according to the dynamic model of the launch system. Subsequently, in view of the problem of “differential explosion” in the backstepping controller design of the seventh-order nonlinear system, a nonlinear dynamic surface control (DSC) framework is proposed. Meanwhile, the time delay estimation (TDE) technique is introduced to suppress the influence of the complex nonlinearities of the launch system on the missile attitude control, and a nonlinear robust control (NRC) is introduced to attenuate the TDE error. Both of these are integrated into the DSC framework, which can achieve asymptotic output tracking. Finally, numerical simulations are conducted to validate the superiority of the proposed control strategy in regards to missile launch response control. Full article
(This article belongs to the Special Issue Motion Planning, Trajectory Prediction, and Control for Robotics)
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17 pages, 5063 KiB  
Article
Observer-Based Adaptive Robust Force Control of a Robotic Manipulator Integrated with External Force/Torque Sensor
by Zixuan Huo, Mingxing Yuan, Shuaikang Zhang and Xuebo Zhang
Actuators 2025, 14(3), 116; https://doi.org/10.3390/act14030116 - 27 Feb 2025
Viewed by 531
Abstract
Maintaining precise interaction force in uncertain environments characterized by unknown and varying stiffness or location is significantly challenging for robotic manipulators. Existing approaches widely employ a two-level control structure in which the higher level generates the command motion of the lower level according [...] Read more.
Maintaining precise interaction force in uncertain environments characterized by unknown and varying stiffness or location is significantly challenging for robotic manipulators. Existing approaches widely employ a two-level control structure in which the higher level generates the command motion of the lower level according to the force tracking error. However, the low-level motion tracking error is generally ignored completely. Recognizing this limitation, this paper first formulates the low-level motion tracking error as an unknown input disturbance, based on which a dynamic interaction model capturing both structured and unstructured uncertainties is developed. With the developed interaction model, an observer-based adaptive robust force controller is proposed to achieve accurate and robust force modulation for a robotic manipulator. Alongside the theoretical stability analysis, comparative experiments with the classical admittance control (AC), the adaptive variable impedance control (AVIC), and the adaptive force tracking admittance control based on disturbance observer (AFTAC) are conducted on a robotic manipulator across four scenarios. The experimental results demonstrate the significant advantages of the proposed approach over existing methods in terms of accuracy and robustness in interaction force control. For instance, the proposed method reduces the root mean square error (RMSE) by 91.3%, 87.2%, and 75.5% in comparison to AC, AVIC, and AFTAC, respectively, in the experimental scenario where the manipulator is directed to follow a time-varying force while experiencing significant low-level motion tracking errors. Full article
(This article belongs to the Special Issue Motion Planning, Trajectory Prediction, and Control for Robotics)
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