Advanced Control of Complex Dynamical Systems and Robotics with Applications, 2nd Edition

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 475

Special Issue Editors


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Guest Editor
Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai 200093, China
Interests: distributed control; adaptive control; robotics
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai 200093, China
Interests: biped robots; dynamic walking; nonlinear circuits; complex systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Mathematics, Hohai University, Nanjing 210098, China
Interests: nonlinear control; fuzzy intelligent control; underactuated system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue showcases the latest research on the use of mathematical models and methods in the analysis and control of complex dynamical systems and robots.

These articles explore a range of topics, including nonlinear dynamics, adaptive control, optimal control, distributed control, and learning control. They present new theoretical results and practical applications that demonstrate the dynamics and control of complex systems and robots.

Specific applications of these techniques in different complex systems and robotic domains are addressed. The topics include, but are not limited to, the following:

  • The modelling and control of nonlinear systems using differential geometry and Lie group methods;
  • Designing adaptive controllers for uncertain systems using Lyapunov stability theory and sliding mode control techniques;
  • Robotic systems using optimal control theory and numerical optimization methods;
  • Learning control policies for autonomous robots using machine learning algorithms and reinforcement learning techniques;
  • Learning cross-domain knowledge for robotic interactions and comprehension using machine learning algorithms and large pretrained language models.

We hope that this Special Issue will serve as a valuable resource for researchers and practitioners interested in this field. The articles provide a glimpse into the diverse range of mathematical techniques that can be applied to these fields, while also highlighting the importance of collaboration between mathematicians and engineers in addressing real-world challenges.

Dr. Gang Wang
Prof. Dr. Qingdu Li
Prof. Dr. Hua Chen
Guest Editors

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Keywords

  • modelling and control of robotics
  • machine learning algorithms
  • complex systems and stability analysis
  • distributed control of multiagent system

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Published Papers (1 paper)

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Research

21 pages, 2102 KiB  
Article
ZNN-Based Gait Optimization for Humanoid Robots with ALIP and Inequality Constraints
by Yuanji Liu, Hao Jiang, Haiming Mou, Qingdu Li and Jianwei Zhang
Mathematics 2025, 13(6), 954; https://doi.org/10.3390/math13060954 - 13 Mar 2025
Viewed by 371
Abstract
This paper presents a zeroing neural networks (ZNN)-based gait optimization strategy for humanoid robots. First, the algorithm converts the angular momentum linear inverted pendulum (ALIP)-based gait planning problem into a time-varying quadratic programming (TVQP) problem by adding adaptive adjustment factors and physical limits [...] Read more.
This paper presents a zeroing neural networks (ZNN)-based gait optimization strategy for humanoid robots. First, the algorithm converts the angular momentum linear inverted pendulum (ALIP)-based gait planning problem into a time-varying quadratic programming (TVQP) problem by adding adaptive adjustment factors and physical limits as inequality constraints to avoid system oscillations or instability caused by large fluctuations in the robot’s angular momentum. Secondly, This paper proposes a real-time and efficient solution for TVQP based on an integral strong predefined time activation function zeroing neural networks (ISPTAF-ZNN). Unlike existing ZNN approaches, the proposed ISPTAF-ZNN is enhanced to achieve convergence within a strong predefined-time while exhibiting noise tolerance. This ensures the desired rapid convergence and resilience for applications requiring strict time efficiency. The theoretical analysis is conducted using Lyapunov stability theory. Finally, the comparative experiments verify the convergence, robustness, and real-time performance of the ISPTAF-ZNN in comparison with existing ZNN approaches. Moreover, comparative gait planning experiments are conducted on the self-built humanoid robot X02. The results demonstrate that, compared to the absence of an optimization strategy, the proposed algorithm can effectively prevent overshoot and approximate energy-efficient responses caused by large variations in angular momentum. Full article
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