期刊信息

Control Engineering Practice

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影响因子:
5.3
出版商:
Elsevier
ISSN:
0967-0661
浏览:
42807
关注:
3
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征稿

Control Engineering Practice is an academic journal published by Elsevier. (ISSN 0967-0661, impact factor 5.3).

Aims & Scope A journal of IFAC, The International Federation of Automatic Control. Control Engineering Practice strives to meet the needs of industrial practitioners and industrially related academics and researchers. It publishes papers which illustrate the direct application of control theory and its supporting tools in all possible areas of automation. As a result, the journal only contains papers which can be considered to have made significant contributions to the application of advanced control techniques. It is normally expected that practical results should be included, but where simulation only studies are available, it is necessary to demonstrate that the simulation model is representative of a genuine application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice's sister publication, Automatica. It is also expected that papers are innovative with respect to the state of the art and are sufficiently detailed for a reader to be able to duplicate the main results of the paper (supplementary material, including datasets, tables, code and any relevant interactive material can be made available and downloaded from the website). The benefits of the presented methods must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods. Moreover, a thorough analysis of failures that may happen in the design process and implementation can also be part of the paper. The scope of Control Engineering Practice matches the activities of IFAC. Papers demonstrating the contribution of automation and control in improving the performance, quality, productivity, sustainability, resource and energy efficiency, and the manageability of systems and processes for the benefit of mankind and are relevant to industrial practitioners are most welcome. Fields of applications in control and automation: •Automotive Systems •Aerospace Applications •Marine Systems •Intelligent Transportation Systems and Traffic Control •Autonomous Vehicles •Robotics •Human Machine Systems •Mechatronic Systems •Scientific Instrumentation •Micro- and Nanosystems •Fluid Power Systems •Gas Turbines and Fluid Machinery •Machine Tools •Manufacturing Technology and Production Engineering •Logistics •Power Electronics •Electrical Drives •Internet of Things •Communication Systems •Power and Energy Systems •Biomedical Engineering and Medical Applications •Biosystems and Bioprocesses •Biotechnology •Chemical Engineering •Pulp and Paper Processing •Mining, Mineral and Metal Processing •Water/Gas/Oil Reticulation Systems •Environmental Engineering •Agricultural Systems •Food Engineering •Other Emerging Control Applications Applicable methods, theories and technologies: •Modeling, Simulation and Experimental Model Validation •System Identification and Parameter Estimation •Observer Design and State Estimation •Soft Sensing •Sensor Fusion •Optimization •Adaptive and Robust Control •Learning Control •Nonlinear Control •Control of Distributed-Parameter Systems •Model-based Control Techniques •Optimal Control and Model Predictive Control •Controller Tuning •PID Control •Feedforward Control and Trajectory Planning •Networked Control •Stochastic Systems •Fault Detection and Isolation •Diagnosis and Supervision •Actuator and Sensor Design •Measurement Technology in Control •Software Engineering Techniques •Real-time and Distributed Computing •Intelligent Components and Instruments •Architectures and Algorithms for Control •Real-time Algorithms •Computer-aided Systems Analysis and Design •Implementation of Automation Systems •Machine Learning •Artificial Intelligence Techniques •Discrete Event and Hybrid Systems •Production Planning and Scheduling •Automation •Data Mining •Data Analytic •Performance Monitoring •Experimental Design •Other Emerging Control Theories and Related Technologies
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特刊

特刊:AI-Driven Control and Planning for Advanced Robotics 截稿日期: 2027-05-31 The convergence of Artificial Intelligence (AI) and robotics has reached a critical inflection point. While classical control theory provides rigorous guarantees for stability and robustness, AI-driven control and planning - including Foundation Models, Large Language Models (LLMs), World Model (WM), and Vision-Language-Action (VLA) architectures, Reinforcement learning, Imitation Learning - offers significant potential for robots to operate in unstructured and semantics-rich environments.This Special Issue aims to provide a focused platform for original research that advances AI-driven robotic control and planning while maintaining the precision, reliability, and safety required by control engineering practice, particularly in combination with LLMs. Contributions are expected to demonstrate practical applicability, experimental validation, or highly representative real-world simulations, and to compare proposed methods against established baselines. Consistent with the mission of Control Engineering Practice, papers should emphasize practical relevance, methodological contribution, robustness, deployment issues, and benefits over existing approaches, rather than purely conceptual AI formulations. Foundation-model-enabled robot planning and control.​ Multimodal perception-action architectures. Integration of AI/LLMs with model-based frameworks (e.g., Neural MPC, Control Barrier Functions, and Lyapunov-stable learning). Closed-loop execution with language or vision-language guidance. Control-aware policy refinement and verification. Failure analysis, supervision, and safety mechanisms for AI-enabled robotic systems. Advanced learning-based control/planning (e.g., reinforcement learning, imitation learning, neural controller, neural planner) that outperforms traditional algorithms. Safety-critical shielding, formal methods for neural control loops, and robustness analysis of AI-driven systems. Sim-to-real transfer with representative validation. Domain-specific robotic applications in industrial automation, autonomous vehicles, logistics, field robotics, and biomedical or medical robotic systems. Guest editors: Prof. Yingbai Hu Affiliation: Hunan University, Changsha, China Prof. Hongtian Chen Affiliation: Shanghai Jiao Tong University, Shanghai, China Prof. Jiming Chen Affiliation: Zhejiang University, Hangzhou, China Prof. Chun-Yi Su Affiliation: Concordia University, Montreal, Canada Prof. Leonardo S. Mattos Affiliation:Istituto Italiano di Tecnologia, Genoa, Italy Manuscript submission information: Open for Submission: from 01-Oct-2026 to 31-May-2027 Article Type Name: "VSI: CONPRA_AI-Driven Control" - please select this item when you submit manuscripts online All manuscripts will be peer-reviewed. Submissions will be evaluated based on originality, significance, technical quality, and clarity. Once accepted, articles will be posted online immediately and published in a journal regular issue within weeks. Articles will also be simultaneously collected in the online special issue. For any inquiries about the appropriateness of contribution topics, please contact the Leading Guest Editor (Yingbai Hu). Keywords: AI-driven control; Foundation models; Large language models; Vision-language-action architectures; RL/IL based control/planning; Neural control; Sim-to-real transfer; Industrial and medical robotics https://www.sciencedirect.com/special-issue/335109/ai-driven-control-and-planning-for-advanced-robotics
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