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Electronics
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Advances in Intelligent Computing and Systems Design
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Advances in Intelligent Computing and Systems Design

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 15 March 2026 | Viewed by 900

Special Issue Editors

Dr. Jiaping Xiao

E-Mail Website
Guest Editor
School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
Interests: cyber-physical systems; intelligent systems; machine vision; multirobot learning
Dr. Xiaoyuan Zheng

E-Mail Website
Guest Editor
School of Artificial Intelligence and Data Science, Hebei University of Technology, No. 5340 Xiping Road, Beichen District, Tianjin 300401, China
Interests: multi-agent systems; distributed filtering; data-driven control; nonlinear system control
Dr. Lu Bai

E-Mail Website
Guest Editor
School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
Interests: control theory; distributed optimization; multi-task learning
Dr. Heling Yuan

E-Mail
Guest Editor
Center for Power Engineering, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: power system stability and operation; onshore microgrid energy management systems

Special Issue Information

Dear Colleagues,

The convergence of artificial intelligence, adaptive computation, and physical embodiment is giving rise to a new era of intelligent systems that are capable of interacting seamlessly with the world. From autonomous robots and drones to edge AI devices and smart materials, embodied intelligence is transforming how machines perceive, decide, and act within complex, real-world environments. This Special Issue aims to explore cutting-edge research at the interface of intelligent computing and systems engineering, with a particular emphasis on the role of embodiment in enabling adaptive, robust, and context-aware intelligent behavior. We welcome interdisciplinary contributions that advance the design, computation, integration, and application of intelligent systems across physical, cyber-physical, and biological domains.

Topics of interests include, but are not limited to, the following:

  • Learning-enabled control and decision-making for embodied agents;
  • Neuromorphic computing and brain-inspired architectures;
  • Edge AI and embedded systems for real-time perception and inference;
  • Multi-agent and swarm intelligence for cooperative system design;
  • Hardware–software co-design for robotics, wearables, and autonomous vehicles;
  • Bio-inspired and morphologically adaptive systems;
  • Robust and resilient computing for uncertain and dynamic environments.

Dr. Jiaping Xiao
Dr. Xiaoyuan Zheng
Dr. Lu Bai
Dr. Heling Yuan
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly 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

  • embodied intelligence
  • intelligent systems design
  • neuromorphic computing
  • swarm and multi-agent systems
  • cyber-physical systems
  • edge AI and embedded systems
  • resilient and robust AI

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

Published Papers (2 papers)

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Research

23 pages, 4040 KB  
Article
Energy-Efficient Train Control Based on Energy Consumption Estimation Model and Deep Reinforcement Learning
by Jia Liu, Yuemiao Wang, Yirong Liu, Xiaoyu Li, Fuwang Chen and Shaofeng Lu
Electronics 2025, 14(24), 4939; https://doi.org/10.3390/electronics14244939 - 16 Dec 2025
Viewed by 69
Abstract
Energy-efficient Train Control (EETC) strategy needs to meet safety, punctuality, and energy-saving requirements during train operation, and puts forward higher requirements for online use and adaptive ability. In order to meet the above requirements and reduce the dependence on an accurate mathematical model [...] Read more.
Energy-efficient Train Control (EETC) strategy needs to meet safety, punctuality, and energy-saving requirements during train operation, and puts forward higher requirements for online use and adaptive ability. In order to meet the above requirements and reduce the dependence on an accurate mathematical model of train operation, this paper proposes a train-speed trajectory-optimization method combining data-driven energy consumption estimation and deep reinforcement learning. First of all, using real subway operation data, the key unit basic resistance coefficient in train operation is analyzed by regression. Then, based on the identified model, the energy consumption experiment data of train operation is generated, into which Gaussian noise is introduced to simulate real-world sensor measurement errors and environmental uncertainties. The energy consumption estimation model based on a Backpropagation (BP) neural network is constructed and trained. Finally, the energy consumption estimation model serves as a component within the Deep Deterministic Policy Gradient (DDPG) algorithm environment, and the action adjustment mechanism and reward are designed by integrating the expert experience to complete the optimization training of the strategy network. Experimental results demonstrate that the proposed method reduces energy consumption by approximately 4.4% compared to actual manual operation data. Furthermore, it achieves a solution deviation of less than 0.3% compared to the theoretical optimal baseline (Dynamic Programming), proving its ability to approximate global optimality. In addition, the proposed algorithm can adapt to the changes in train mass, initial set running time, and halfway running time while ensuring convergence performance and trajectory energy saving during online use. Full article
(This article belongs to the Special Issue Advances in Intelligent Computing and Systems Design)
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16 pages, 1719 KB  
Article
Gait Generation and Motion Implementation of Humanoid Robots Based on Hierarchical Whole-Body Control
by Helin Wang and Wenxuan Huang
Electronics 2025, 14(23), 4714; https://doi.org/10.3390/electronics14234714 - 29 Nov 2025
Viewed by 476
Abstract
Attempting to make machines mimic human walking, grasping, balancing, and other behaviors is a deep exploration of cognitive science and biological principles. Due to the existing prediction lag problem, an error compensation mechanism that integrates historical motion data is proposed. By constructing a [...] Read more.
Attempting to make machines mimic human walking, grasping, balancing, and other behaviors is a deep exploration of cognitive science and biological principles. Due to the existing prediction lag problem, an error compensation mechanism that integrates historical motion data is proposed. By constructing a humanoid autonomous walking control system, this paper aims to use a three-dimensional linear inverted pendulum model to plan the general framework of motion. Firstly, the landing point coordinates of the single foot support period are preset through gait cycle parameters. In addition, it is substituted into dynamic equation to solve the centroid (COM) trajectory curve that conforms to physical constraints. A hierarchical whole-body control architecture is designed, with a task priority based on quadratic programming solver used at the bottom to decompose high-level motion instructions into joint space control variables and fuse sensor data. Furthermore, the numerical iterative algorithm is used to solve the sequence of driving angles for each joint, forming the control input parameters for driving the robot’s motion. This algorithm solves the limitations of traditional inverted pendulum models on vertical motion constraints by optimizing the centroid motion trajectory online. At the same time, it introduces a contact phase sequence prediction mechanism to ensure a smooth transition of the foot trajectory during the switching process. Simulation results demonstrate that the proposed framework improves disturbance rejection capability by over 30% compared to traditional ZMP tracking and achieves a real-time control loop frequency of 1 kHz, confirming its enhanced robustness and computational efficiency. Full article
(This article belongs to the Special Issue Advances in Intelligent Computing and Systems Design)
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