Perception and Intelligent Control for Cyber–Physical Systems: From Robotics to Autonomous Ecosystems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: 15 March 2026 | Viewed by 657

Special Issue Editor


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Guest Editor
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
Interests: robotics; automation; artificial intelligence; intelligent motion planning and control; learning-based robotic systems; nonlinear control methodologies; trajectory optimization; synergistic integration of AI for decision-making; interactive manipulation in complex environments

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the integrated roles of advanced perception, learning algorithms, and intelligent control in enabling autonomy for robotic and unmanned systems. It aims to showcase cutting-edge research that bridges the gap between theoretical innovation and practical application, emphasizing systems that can operate, adapt, and cooperate in complex, real-world environments.

The scope encompasses a wide range of topics within robotic cyber–physical systems, including, but not limited to, the following:

  • Novel sensing technologies and multi-sensor fusion for environment perception and state estimation.
  • Machine learning and AI-driven control strategies (e.g., deep reinforcement learning, adaptive control).
  • Digital twins for autonomous systems, dynamic modeling and synchronization between physical robots and their digital twins.
  • Twin-guided perception, predictive maintenance, and real-time replanning.
  • Human-in-the-loop interaction and teleoperation via immersive digital twins.
  • Autonomous navigation, decision-making, and motion planning under uncertainty.
  • Multi-robot systems: distributed control, collaborative perception, and swarm intelligence.
  • Human–robot interaction, safe collaboration, and trustworthiness.
  • Real-world applications in areas such as industrial automation, autonomous driving, aerial drones, and healthcare robotics.
  • Middleware and software frameworks for autonomous cyber–physical systems.

The purpose of this collection is to provide a platform for researchers to present high-quality original research and reviews that address both theoretical advances and practical implementations. We seek to highlight how integrated perception and control strategies are pushing the boundaries of what autonomous systems can achieve—from single robots to large-scale cooperating networks.

Dr. Yueyue Liu
Guest Editor

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Keywords

  • intelligent perception and sensing
  • autonomous robotic systems
  • digital twin modeling
  • learning-based control
  • multi-robot coordination
  • cyber–physical systems
  • adaptive control
  • autonomous planning

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Published Papers (2 papers)

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Research

18 pages, 1237 KB  
Article
Real-Time Robotic Navigation with Smooth Trajectory Using Variable Horizon Model Predictive Control
by Guopeng Wang, Guofu Ma, Dongliang Wang, Keqiang Bai, Weicheng Luo, Jiafan Zhuang and Zhun Fan
Electronics 2026, 15(3), 603; https://doi.org/10.3390/electronics15030603 - 29 Jan 2026
Viewed by 120
Abstract
This study addresses the challenges of real-time performance, safety, and trajectory smoothness in robot navigation by proposing an innovative variable-horizon model predictive control (MPC) scheme that utilizes evolutionary algorithms. To effectively adapt to the complex and dynamic conditions during navigation, a constrained multi-objective [...] Read more.
This study addresses the challenges of real-time performance, safety, and trajectory smoothness in robot navigation by proposing an innovative variable-horizon model predictive control (MPC) scheme that utilizes evolutionary algorithms. To effectively adapt to the complex and dynamic conditions during navigation, a constrained multi-objective evolutionary algorithm is used to tune the control parameters precisely. The optimized parameters are then used to dynamically adjust the MPC’s prediction horizon online. To further enhance the system’s real-time performance, warm start and multiple shooting techniques are introduced, significantly improving the computational efficiency of the MPC. Finally, simulation and real-world experiments are conducted to validate the effectiveness of the proposed method. Experimental results demonstrate that the proposed control scheme exhibits excellent navigation performance in differential-drive robot models, offering a novel solution for intelligent mobile robot navigation. Full article
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14 pages, 2530 KB  
Article
Arrester Fault Recognition Model Based on Thermal Imaging Images Using VMamba
by Lin Lin, Jiantao Li, Jianan Wang, Yong Luo and Yueyue Liu
Electronics 2025, 14(24), 4784; https://doi.org/10.3390/electronics14244784 - 5 Dec 2025
Viewed by 287
Abstract
The intelligent fault detection of power plant equipment in industrial settings often grapples with challenges such as insufficient real-time performance and interference from complex backgrounds. To address these issues, this paper proposes an image recognition and classification model based on the VMamba architecture. [...] Read more.
The intelligent fault detection of power plant equipment in industrial settings often grapples with challenges such as insufficient real-time performance and interference from complex backgrounds. To address these issues, this paper proposes an image recognition and classification model based on the VMamba architecture. At the core of our feature extraction module, we have improved and optimized the two-dimensional state space (SS2D) algorithm to replace the traditional convolution operation. Rooted in State-Space Models (SSMs), the SS2D module possesses a global receptive field by design, enabling it to effectively capture long-range dependencies and establish comprehensive contextual relationships between local and global features. Crucially, unlike the self-attention mechanism in Vision Transformers (ViT) that suffers from quadratic computational complexity, VMamba achieves this global modeling with linear complexity, significantly enhancing computational efficiency. Furthermore, we employ an enhanced PAN-FPN multi-scale feature fusion strategy integrated with the Squeeze-and-Excitation (SE) attention mechanism. This combination optimizes the spatial distribution of feature representations through channel-wise attention weighting, facilitating the effective integration of cross-level spatial features and the suppression of background noise. This study thus presents a solution for industrial equipment fault diagnosis that achieves a superior balance between high accuracy and low latency. Full article
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