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Machines
Special Issues
Bridging the Control Theory, Optimization, and Learning: Application in Robotics
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Bridging the Control Theory, Optimization, and Learning: Application in Robotics

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machine Design and Theory".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 1017

Special Issue Editor

Dr. Bin Wei

E-Mail Website
Guest Editor
Department of Mechanical and Industrial Engineering, Texas A & M University, Kingsville, TX 78363, USA
Interests: robotics; control theory; nonlinear dynamics; human-robot interaction; advanced manufacturing; synchronization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Control practitioners are trying to understand how optimization and reinforcement learning enable the application of control theory to achieve better control performance in the presence of uncertainty and high dimensionality. Reinforcement learning is trying to find a path to improve the control field. In light of this context, this Special Issue focuses on building a connection between control theory, optimization, and reinforcement learning, before finally applying it to robotics. Papers in the fields of Lyapunov function and value function, stability and optimality, and embedding optimization into control and its application in robotics are particularly welcome, though papers in other fields will also be accepted.

Dr. Bin Wei
Guest Editor

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. Machines 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

  • Lyapunov function
  • value function
  • robotics
  • optimization
  • nonlinear control
  • MPC

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Published Papers (1 paper)

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Research

24 pages, 2070 KiB  
Article
Reinforcement Learning-Based Finite-Time Sliding-Mode Control in a Human-in-the-Loop Framework for Pediatric Gait Exoskeleton
by Matthew Wong Sang and Jyotindra Narayan
Machines 2025, 13(8), 668; https://doi.org/10.3390/machines13080668 - 30 Jul 2025
Viewed by 158
Abstract
Rehabilitation devices such as actuated lower-limb exoskeletons can provide essential mobility assistance for pediatric patients with gait impairments. Enhancing their control systems under conditions of user variability and dynamic disturbances remains a significant challenge, particularly in active-assist modes. This study presents a human-in-the-loop [...] Read more.
Rehabilitation devices such as actuated lower-limb exoskeletons can provide essential mobility assistance for pediatric patients with gait impairments. Enhancing their control systems under conditions of user variability and dynamic disturbances remains a significant challenge, particularly in active-assist modes. This study presents a human-in-the-loop control architecture for a pediatric lower-limb exoskeleton, combining outer-loop admittance control with robust inner-loop trajectory tracking via a non-singular terminal sliding-mode (NSTSM) controller. Designed for active-assist gait rehabilitation in children aged 8–12 years, the exoskeleton dynamically responds to user interaction forces while ensuring finite-time convergence under system uncertainties. To enhance adaptability, we augment the inner-loop control with a twin delayed deep deterministic policy gradient (TD3) reinforcement learning framework. The actor–critic RL agent tunes NSTSM gains in real-time, enabling personalized model-free adaptation to subject-specific gait dynamics and external disturbances. The numerical simulations show improved trajectory tracking, with RMSE reductions of 27.82% (hip) and 5.43% (knee), and IAE improvements of 40.85% and 10.20%, respectively, over the baseline NSTSM controller. The proposed approach also reduced the peak interaction torques across all the joints, suggesting more compliant and comfortable assistance for users. While minor degradation is observed at the ankle joint, the TD3-NSTSM controller demonstrates improved responsiveness and stability, particularly in high-load joints. This research contributes to advancing pediatric gait rehabilitation using RL-enhanced control, offering improved mobility support and adaptive rehabilitation outcomes. Full article
(This article belongs to the Special Issue Bridging the Control Theory, Optimization, and Learning: Application in Robotics)
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