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

Special Issue on New Applications of Data-driven Performance Optimization and Safety Assessment for Large-scale Systems
Submission Date: 2024-12-01

Nowadays, automation systems have undergone significant advancements, evolving into increasingly large-scale integration. Aided by sophisticated automatic control, communication networks and perceptual units, multiple sub-systems can collaborate seamlessly to carry out and complete sophisticated tasks. Granting performance optimization and safety assessment (POSA) is of paramount importance for these large-scale systems, serving as a fundamental prerequisite for system functionality.

The increasing demands for POSA in large-scale systems have received significant attention. More specifically, the control community has obtained major achievements in developing intelligent POSA algorithms, by assuming the availability of well-established system models (i.e., accurate physical-based models of large-scale systems as with their functional interconnection topology). However, this “a priori system knowledge” assumption has pros and cons. On the one hand, it simplifies the theoretical analysis, thereby improving our understanding on large-scale systems. On the other hand, challenges inevitably emerge when the POSA algorithms come to practical implementation. Fortunately, the rapid advancements in artificial intelligence have paved the way for data-driven POSA-based solutions. These approaches utilize heterogeneous data to extract system knowledge, providing an alternative perspective on the dynamic behaviors of large-scale systems. It follows that data-driven POSA designs have emerged as an efficient and promising method for addressing POSA tasks in large-scale systems. Despite these remarkable developments, there still exists a considerable gap between theoretical research and the practical application of POSA algorithms for large-scale systems. Bridging this gap remains a crucial task that requires further exploration and integration of cutting-edge research with real-world scenarios.
This special issue intends to collect novel temporally and spatially data-driven POSA designs, methodologies, methods and applications of large-scale automation systems by considering network communication, system dynamics, intelligent perception and decisions, to name the few investigation areas.

Guest editors:
Prof. Hongtian Chen (Executive Guest Editor)Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China.Email: hongtian.chen@sjtu.edu.cn 
Prof. Yalin WangSchool of Automation, Central South University, Changsha 410083, China. Email: ylwang@csu.edu.cn
Prof. Cesare AlippiUniversità della Svizzera italiana, Switzerland, and Politecnico di Milano, Italy. Email: cesare.alippi@polimi.it
Prof. Bin JiangCollege of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China. Email: binjiang@nuaa.edu.cn
Prof. Marios M. PolycarpouElectrical and Computer Engineering, University of Cyprus, Cyprus.Email: mpolycar@ucy.ac.cy
Special issue information:

Topics of interest to this special issue include, but are not limited to:

Performance optimization for large-scale systems
Safety and reliability assessment for large-scale systems
Data-driven performance recovery for large-scale systems
Model-free resilient control for large-scale systems
Knowledge-based fault diagnosis for large-scale systems
Heterogeneous POSA designs for large-scale systems
Computer vision-aided optimization for large-scale systems
Data-driven lifecycle management for large-scale systems
System maintenance for large-scale systems
Online adaptive learning for large-scale systems
Artificial intelligence for large-scale systems 

Special Issue on UAV Control and Applications in the Low-Altitude Economy
Submission Date: 2024-12-31

The low-altitude economy (LAE) represents a rapidly growing sector where Unmanned Aerial Vehicles (UAVs) are revolutionizing various industries, including logistics, agriculture, surveillance, and environmental monitoring. As UAV technology continues to advance, its integration into these sectors is becoming increasingly critical for efficiency, cost-effectiveness, and innovation. The success of UAV applications heavily depends on sophisticated control systems that can handle the diverse and complex constraints inherent in low-altitude operations.

The importance of the low-altitude economy cannot be overstated. With the growing demand for real-time data, efficient delivery systems, and precise agricultural practices, UAVs are positioned to become indispensable tools. However, the effective deployment of UAVs in these applications requires robust control algorithms that can manage the dynamic and unpredictable nature of low-altitude environments. Additionally, the collaboration between UAVs and ground vehicles offers promising opportunities for enhanced data collection, monitoring, and operational efficiency. This special issue aims to attract researchers to share their latest findings, innovative solutions, and practical implementations in the realm of UAV control and applications within the low-altitude economy.

Guest editors:

Research Assist. Prof. Chao Huang (Executive Guest Editor)
The Hong Kong Polytechnic University, Hong Kong, SAR
Email: hchao.huang@polyu.edu.hk 
Areas of expertise: UAV control and application, human-machine collaboration, intelligent system modelling.

Assist. Prof. Laurent Burlion
Rutgers, the State University of New Jersey, USA
Email: laurent.burlion@rutgers.edu 
Areas of expertise: control theory, aircraft control, visual servoing, nonlinear control, robust control

Prof. Andrey Savkine
University of New South Wales, Sydney, Australia
Email: a.savkin@unsw.edu.au 
Areas of expertise: Control Engineering, Robot Navigation, Biomedical Engineering, Power Systems

Special issue information:

This special issue seeks contributions that address various aspects of UAV control and their applications in the low-altitude economy. Topics of interest include, but are not limited to:

    Ground Infrastructure Planning for UAV Operations
    Low-Altitude Airspace Structure Design
    Traffic management strategies for UAVs
    Low-Altitude Airspace Management Systems
    Real-time airspace monitoring and control
    Navigation Control for Multi-UAV Enabled Mobile Internet of Vehicles
    Path planning and optimization for multiple UAVs
    Interaction between UAVs and ground vehicles
    Perception Positioning Control for UAV Teams
    Real-time perception and positioning
    Cooperative localization and mapping
    Communication Control for Multi-UAV Assisted Mobile Internet of Vehicles
    Networked control systems for UAVs
    UAV package delivery

Please note that Control Engineering Practice publishes papers which make significant contributions to the application of control techniques and are expected to contain practically relevant results based on profound theory. CEP is an applications-oriented journal. Therefore, we publish papers providing application-related information, stressing the relevance of the work in a practical industrial/applications context, with solid industrial examples rather than hypothetical ones. If only simulations have been used, these must be verified on models of real plants. The benefits must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods.

Manuscript submission information:

Manuscripts should be submitted via the Control Engineering Practice online submission system (https://www.editorialmanager.com/conengprac/default.aspx.) by selecting the Article Type of “VSI: Low-altitude economy”. All submitted manuscripts will be screened by the editorial office and peer reviewed according to the usual standards of this journal and will be evaluated on the basis of originality, quality, and relevance to this Special Issue. Please also note the IFAC publication policy: Papers submitted to IFAC journals with prior publication in any copy righted conference proceedings must be substantially different from the conference publication. Authors should indicate in the cover letter in detail how the journal paper differs from the relevant conference paper or papers. In particular, the additional original contribution in the journal paper has to be pointed out explicitly. In the journal paper, the conference paper has to be cited and discussed as any other paper in the list of references.

Tentative Schedule:

    Submission deadline: December 31st, 2024
    Acceptance deadline: July 31st, 2025

For more information or questions, please feel free to contact the Guest Editors.

Keywords:

UAV, control theory, low-altitude economy, Multi-UAV Enabled Mobile Internet of Vehicles

Special Issue on Nexus of Power and Transport Decarbonization
Submission Date: 2025-01-20

The transport and power sectors are the two major sources of carbon emissions, accounting for approximately 60% of the global total carbon emissions. Accelerating the decarbonization of these two sectors will be crucial for achieving the net zero target by the mid of this century committed by over 145 countries and regions. These two sectors are capital intensive critical national infrastructures and siloed approaches to decarbonize the two closely intertwined sectors will be extremely costly and timeconsuming.

On one hand, the power sector needs to meet the increasing demand resulting from rapid transportation electrification and decarbonization, while the existing power network has already been stretched to its capacity. On the other hand, transport electrification and decarbonization may potentially bring a significant amount of flexibility to the power network operation. This symbiotic relationship between power and transport sectors along with net zero transition will present a variety of new challenges and opportunities for control engineers.

Guest editors:

Prof. Kang Li (Executive Guest Editor)
School of Electronic and Electrical Engineering, University of Leeds, UK
Email: k.li1@leeds.ac.uk 

Prof. Stuart Hillmansen
School of Electronic, Electrical and Systems Engineering, University of Birmingham, UK
Email: s.hillmansen@bham.ac.uk 

Prof. Minwu Chen
School of Electrical Engineering, Southwest Jiaotong University, China
Email: chenminwu@home.swjtu.edu.cn 

Prof. Yong Li
College of Electrical and Information Engineering, Hunan University, China 
Email: yongli@hnu.edu.cn 

Special issue information:

This special issue aims to explore and showcase the application of control engineering technologies in addressing the synergy and potential challenges in decarbonizing both the power and transportation sectors in a whole system approach. Key topics to be addressed in this special issue include:

    Holistic control engineering solutions to decarbonize energy and transport sectors.
    Planning, operation and control of electrified transportation systems for enhancing power grid stability and flexibility.
    Advanced control schemes for integrating renewable energy and electric vehicles into smart grids.
    Intelligent control of powertrain technologies combining with electricity, fuel cell, or other renewable power sources.
    Advanced electric vehicle fleet charging and discharging control to support power grid operations.
    Modeling and control of microgrid and virtual power plant solutions to deliver services to both transport and power sectors.
    Control system design for hybrid energy storage solutions to improve flexibility and resilience of power and transport networks.
    AI and machine learning based control applications in power and transport sectors to accelerate decarbonization.
    Control applications in novel shared mobility systems and platforms.
    Modelling and control applications in digital twinning of transport and power systems.

Please note that Control Engineering Practice publishes papers which make significant contributions to the application of control techniques and are expected to contain practically relevant results based on profound theory. CEP is an applications-oriented journal. Therefore, we publish papers providing application-related information, stressing the relevance of the work in a practical industrial/applications context, with solid industrial examples rather than hypothetical ones. If only simulations have been used, these must be verified on models of real plants. The benefits must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods.

Manuscript submission information:

Manuscripts should be submitted via the Control Engineering Practice online submission system (https://www.editorialmanager.com/conengprac/default.aspx) by selecting the Article Type of “VSI: Power and Transport Decarbonization”. All submitted manuscripts will be screened by the editorial office and peer reviewed according to the usual standards of this journal, and will be evaluated on the basis of originality, quality, and relevance to this Special Issue. Please also note the IFAC publication policy: Papers submitted to IFAC journals with prior publication in any copy righted conference proceedings must be substantially different from the conference publication. Authors should indicate in the cover letter in detail how the journal paper differs from the relevant conference paper or papers. In particular, the additional original contribution in the journal paper has to be pointed out explicitly. In the journal paper, the conference paper has to be cited and discussed as any other paper in the list of references.

Important Dates

    Submission deadline: 20th January, 2025
    Acceptance deadline: 20th August, 2025

Keywords:

Decarbonization of energy and transport systems; AI and machine learning assisted system optimization and control; microgrid design and control; virtual power plant management and control; intelligent control of powertrains; digital twinning

Special Issue on A New Generation of Industrial Artificial Intelligence Driven by Data Knowledge Fusion
Submission Date: 2025-01-31

By extracting process information from the archived historical dataset, data-driven models can be effectively established for industrial systems without first-principle knowledge. However, several problems will confront these conventional data-driven methods. First, the archived historical dataset may be insufficient for developing an accurate industrial model. Second, the generalizable of the established models may be affected when the low-quality data is utilized. Third, the obtained result may difficult to be understood by the industrial practitioners since no process information is involved.

The emergence of interconnection and fusion of data and knowledge promotes industrial artificial intelligence to a new stage. Specifically, the new generation of industrial artificial intelligence, which combines the strengths of machine learning and physics knowledge, can be regarded as the key enabler for smart industry. With the universal approximation capability of machine learning methods, the emerging industrial artificial intelligence can effectively reveal the complex correlation between measurement variables in industrial systems. Take advantage of physics-based modeling information, the emerging industrial artificial intelligence can be adjusted to satisfy the real industrial scenes even when the training data is insufficient or corrupted. Hence, the emerging industrial artificial intelligence helps to achieve the goals of explainable and generalizable smart industry even under harsh environment.

This special issue aims to gather and present recent advancements in the data knowledge driven industrial artificial intelligence and their application to smart systems. Contributions encompassing both theoretical insights and practical applications in domains such as large-scale industrial processes, industrial mechatronics, network-supported industries, cyber-physical systems, and other diverse applications are particularly encouraged. The topics of interest include, but are not limited to, the following

    Data knowledge fusion driven industrial artificial intelligence
    Anomaly detection for time series data
    Remaining useful life prediction
    Operation and scheduling of complex Equipment
    Prognostics health management
    Causality analysis for root cause tracing
    Multi-objective optimization based performance evaluation
    Incremental learning based fault detection and diagnosis models
    Few/Zero-shot learning based theory methods and application

Please note that Control Engineering Practice publishes papers which make significant contributions to the application of control techniques and are expected to contain practically relevant results based on profound theory. CEP is an applications-oriented journal. Therefore, we publish papers providing application-related information, stressing the relevance of the work in a practical industrial/applications context, with solid industrial examples rather than hypothetical ones. If only simulations have been used, these must be verified on models of real plants. The benefits must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods.

Guest editors:

Chunhui Zhao (Executive Guest Editor), Zhejiang University, Hangzhou, China, chhzhao@zju.edu.cn 
Wanke Yu, University of Alberta, Edmonton, Canada,  yuwanke@cug.edu.cn 
Biao Huang, University of Alberta, Edmonton, Canada, biao.huang@ualberta.ca 
Denis Dochain, Universite´ Catholique de Louvain, Louvain-la-Neuve, Belgium, denis.dochain@uclouvain.be 
Furong Gao, Hong Kong University of Science and Technology, Hong Kong, China, kefgao@ust.hk 
Min Xie, City University of Hong Kong, Hong Kong, China, minxie@cityu.edu.hk 

Manuscript submission information: 

Tentative Schedule:

    Submission Open Date: 15th August 2024
    Submission Deadline: 31st January 2025
    Notification of Acceptance: 31st August 2025

Manuscripts must be submitted via Control Engineering Practice online submission system (Editorial Manager®): https://www.editorialmanager.com/conengprac/default.aspx. Please select the article type “VSI: Data Knowledge Fusion” when submitting your manuscript online.

Please refer to the Guide for Authors to prepare your manuscript: https://www.elsevier.com/journals/control-engineering-practice/0967-0661/guide-for-authors. 

For any further information, the authors may contact the Guest Editors.

Keywords:

aritificial intelligence; zero-shot learning; data knowledge fusion; anomaly detection and diagnosis; causality analysis