sustainability-logo

Journal Browser

Journal Browser

Intelligent Control and Robotic Systems for Sustainable Development

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: 30 September 2027 | Viewed by 151

Editor

*
E-Mail Website
Guest Editor
School of Automation, Chongqing University, Chongqing 400044, China
Interests: robotic systems; intelligent control
* Associate researcher

Special Issue Information

Dear Colleagues,

With the rapid development of intelligent manufacturing and smart city construction, robotic systems and intelligent control technologies are becoming core enablers for improving efficiency, reducing energy consumption, and promoting sustainable development. This Special Issue focuses on intelligent control theory, robot system design, navigation optimization, and reinforcement learning applications in sustainable scenarios. It aims to collect high-quality research on energy-efficient robot control, adaptive optimization, multi-agent coordination, and intelligent decision-making under complex environments.

Suggested themes and article types

Original research articles and review papers are welcome. Contributions may include, but are not limited to, the following research areas:

  • Intelligent control theory for sustainable automation;
  • Robotic systems for sustainable manufacturing and industrial applications;
  • Energy-efficient control and optimization of robotic systems;
  • Reinforcement learning and adaptive control for robotics;
  • Intelligent navigation, path planning, and motion optimization;
  • Multi-robot coordination and distributed control;
  • Autonomous decision-making in complex and uncertain environments;
  • Human–robot collaboration for sustainable production;
  • Robotics for smart cities and intelligent transportation systems;
  • Robotics for environmental monitoring and ecological protection;
  • Intelligent control for renewable energy and smart energy systems;
  • Digital twins for sustainable robotic applications;
  • AI-enabled fault diagnosis, predictive maintenance, and system reliability;
  • Sustainable agricultural robotics and resource-efficient robotic operations.

This issue strengthens the link between advanced control/robotics and sustainability by highlighting energy saving, safety, reliability, and low-carbon operation. It will complement existing literature by integrating frontier learning-based control methods with real-world engineering applications, providing new theories and solutions for sustainable intelligent systems.

Dr. Shaoxin Sun
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 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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability 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

  • intelligent control
  • robotic systems
  • sustainable development
  • intelligent navigation
  • reinforcement learning
  • adaptive dynamic programming
  • energy efficiency
  • smart manufacturing
  • autonomous systems
  • multi-robot coordination
  • distributed control
  • digital twins
  • sustainable robotics

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

27 pages, 4383 KB  
Article
CoFFormer: A Collaborative Frequency-Domain-Enhanced Network for Sustainable Wind Power Forecasting Under Non-Stationary Conditions
by Yuanyuan Liu, Zhiguo Xiao, Yujing Guo, Junli Liu, Xinyao Cao, Yanqi Shao, Yangfan Zhou and Ke Wang
Sustainability 2026, 18(16), 8433; https://doi.org/10.3390/su18168433 - 17 Aug 2026
Abstract
Accurate wind power forecasting is essential for renewable-energy accommodation, low-carbon dispatch, and the sustainable operation of modern power systems. However, wind power series exhibit pronounced non-stationarity, strong volatility, and multi-scale evolution, making long-term trends and short-term disturbances difficult to characterize jointly. In addition, [...] Read more.
Accurate wind power forecasting is essential for renewable-energy accommodation, low-carbon dispatch, and the sustainable operation of modern power systems. However, wind power series exhibit pronounced non-stationarity, strong volatility, and multi-scale evolution, making long-term trends and short-term disturbances difficult to characterize jointly. In addition, multi-step forecasting errors tend to accumulate with increasing horizons, degrading model accuracy and stability. To address these issues, this study proposes CoFFormer, a collaborative frequency-domain-enhanced network for non-stationary wind power forecasting. The model reduces input modeling complexity, strengthens collaborative representation of heterogeneous temporal information, and suppresses output-stage error accumulation. Specifically, embedded series decomposition mitigates coupling interference between trend and fluctuation components. Differentiated temporal modeling and dynamic gating then adaptively coordinate the contributions of different feature representations, while frequency-domain residual compensation enhances the recovery of periodic structures and local oscillations. Experiments on ETTh2, wind_speed, WindPower, and Location2 demonstrate strong competitiveness across forecasting horizons. CoFFormer achieves MSE/MAE values of 0.0957/0.2238 and 0.1508/0.2889 on ETTh2 for 12- and 24-step forecasting, and 0.0617/0.1490 and 0.3838/0.3948 on WindPower for 3- and 24-step forecasting, outperforming most baselines. Ablation studies confirm the effectiveness and synergy of each component, providing an effective solution for high-accuracy multi-step forecasting of complex non-stationary wind power series. Full article
(This article belongs to the Special Issue Intelligent Control and Robotic Systems for Sustainable Development)
Show Figures

Figure 1

Back to TopTop