Journal Information
Control Engineering Practice
Impact Factor:

Call For Papers
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 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 industrial application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice's sister publication, Automatica. Control Engineering Practice papers will tend to be shorter, and relevant to industrial readers.

In addition to purely technical applications papers the journal carries papers on topics linked to the application of automation, including social effects, cultural aspects, project planning and system design, and economic and management issues.

The scope of Control Engineering Practice matches the activities of IFAC:

• Aerospace • Marine systems • Communication systems • Biomedical engineering • Pulp and paper processing • Environmental engineering • Scientific instrumentation • Transportation and vehicles • Power generation and other utilities • Mining, mineral and metal processing • Chemical and biotechnical process control • Manufacturing technology and production engineering

The journal covers all applicable technologies:

• Robotics • Identification • Signal processing • Project management • Autonomous vehicles • Powertrains • Computer networking • Modelling and simulation • Human-computer systems • Components and instruments • Adaptive and robust control • Electromechanical components • Model-based control techniques • Fault detection and diagnostics • Software engineering techniques • Hydraulic and pneumatic components • Real-time and distributed computing • Intelligent components and instruments • Architectures and algorithms for control • Computer-aided systems analysis and design • Software design, verification, safety, etc. • Artificial intelligence techniques, including fuzzy control neural networks and genetic algorithms.
Last updated by Dou Sun in 2019-12-04
Special Issues
Special Issue on Distributed Networked Industrial Systems: Artificial Intelligence-Based Coordination Control and Cyber Security
Submission Date: 2020-11-30

With rapid development of advanced technologies, such as intelligent sensing and monitoring, communication networks, cloud/fog computing, big data analysis, artificial intelligence and so on, industrial control systems have been experiencing a huge revolution from a single point-to-point control fashion to a networked coordination control fashion. This revolution of control structures offers remarkable merits in improving reliability, efficiency, functionality, scalability and adaptability of systems. As a result, many practical applications of coordination control can be extensively found, especially in the fields of smart grids, intelligent transportation systems, unmanned aerial vehicles, autonomous underwater vehicles, smart factories and so on. For practical networked industrial control systems, the large-scale integration of nodes and the tight coupling of cyber and physical elements become the current trend, which poses new technical and theoretical challenges in distributed coordination control design and implementation. These challenges focus mainly on how to effectively undertake intelligent control decisions and actions based on useful and reliable information refined from massive data and against various malicious cyber attacks. To confront these challenges, much effort has been made by researchers to accommodate the ever-increasing demand for guaranteeing data efficiency and reliability of networked industrial control systems by building on new techniques such as artificial intelligence based control, big data cloud computing, cyber security and so on. Therefore, this Special Issue aims to seek a series of the latest achievements in artificial intelligence and cybersecurity techniques-based coordination control for distributed networked industrial systems, furthermore, contributing to stimulating more interest of researchers in this field. Potential topics of interest include, but are not limited to: Artificial intelligence-based coordination control of distributed networked industrial systems Distributed coordination control and power management in energy internet Platoon control of intelligent transportation systems Attack-resilient distributed coordination control of networked industrial systems Data-driven distributed coordination control of networked industrial systems Cybersecurity and privacy-preserving techniques for networked industrial systems Cloud-aided distributed coordination control of networked industrial systems Distributed formation control of autonomous underwater vehicles Distributed sensing, prediction, and estimation over wireless sensor networks Various applications of coordination control of distributed networked industrial systems Control Engineering Practice is a premier IFAC journal that publishes papers with direct applications of profound control theory and its supporting tools in all possible areas of automation.
Last updated by Dou Sun in 2020-09-07
Special Issue on Smart Technologies for Net-zero Emissions Energy Systems
Submission Date: 2020-12-30

The future global economy will be greatly shaped by the transformed energy landscape and a net-zero transition in energy-intensive processes and systems. While significant progress has already been made towards renewable and clean electric power generation and electrification of heating and on-road light vehicles, an increasing number of additional sectors are facing major challenges in electrification and decarbonisation. These include e.g., high-energy manufacturing and heavy-duty transportation. At the same time, large uncertainties and variabilities in both energy supply and energy demand are becoming a norm, with climate change and unexpected natural and public health disasters and risks further aggravating the existing challenges. These developments call for both technological innovations in different sectors and the coordinated integration of the whole energy chain from top to tail. As an enabling technology, control engineering can play a paramount role in successfully establishing the forthcoming green energy era by accelerating sectoral decarbonisation and creating synergy effects across decarbonisation pathways for different systems. This special issue aims to showcase the latest developments of smart technologies in modelling, control and optimization of hybrid energy systems across different sectors, with a focus on their synergy to deliver the net-zero emission target. Practical contributions towards control engineering and applications are invited on topics that include, but are not limited to: Advanced modelling, scheduling, operation and control techniques for accelerating the use of renewable energy in manufacturing; supporting renewable power integration with on-road transport electrification infrastructure; electrification and energy storage in maritime shipping; integration of renewable power generation and energy storage with railway electrification; Interpretable artificial intelligence and immersive virtual reality to improve the resilience and responsiveness of manufacturing processes for a net zero transition; New sensing and IoT techniques for integration of smart energy systems with intelligent manufacturing; Planning, operation and control of district heating and cooling combined with renewable energy sources; Power electronic control in renewable power generation and their integration with transport electrification; Case studies and emerging industrial applications to promote net-zero transitions.
Last updated by Dou Sun in 2020-09-07
Special Issue on Machine learning and Advanced Data Analytics in Control Engineering Practice
Submission Date: 2020-12-31

We are currently at the cusp of the fourth industrial revolution (4IR) or Industry 4.0, which is poised to reshape the economy and society with unprecedented depth and breadth. Emerging technologies including complex organization and systems, smart sensing, industrial robotics, industrial wireless communications, industrial Internet-of-Things (IIoT), Internet-of-Moving-Things (IoMT), industrial cloud, big data and cyber-physical systems (CPS) have become the hotspots of research and innovation globally. In the last few years, these emerging technologies have become mainstream and industrially-relevant due to continuous advancements in digitalization, artificial intelligence (AI), advanced analytics, massive computing power, inexpensive memory, and the gigantic volumes of data collected. The process industries are in a unique position to benefit from Industry 4.0, as they have the right infrastructure and own massive amounts of heterogeneous industrial data. Industry 4.0 is poised to provide economic and competitive advantages in the face of ever-increasing demands on energy, environment, and quality by providing automation and efficiency never seen before. Process industries have been using data analytics (e.g., principal component analysis (PCA), partial least squares (PLS), canonical variate analysis (CVA), and time-series methods for modeling) in various forms for more than three decades. Recent developments in AI, machine learning, and advanced analytics provide a new opening for leveraging industrial data for solving complex systems engineering problems. Building upon the success of the first special issue onMachine learning and Advanced Data Analytics in Control Engineering Practice, we are happy to release theCall-for-Papers (CfP) for the second special issue on the same topic.The second special issue intends to continue to curate novel advances in the development and application of machine learning techniques to address ever-present challenges of dealing with complex and heterogeneous industrial data in process systems engineering and beyond. Practical contributions are invited on topics that include, but are not limited to: Data analytics and machine learning methods for modeling, control, and optimization; Reinforcement-learning/deep-learning methods for modeling and control; Advanced methods for process data visualization; Natural language processing/computer-vision/speech-recognition in the process industries; Adaptive methods for autonomous learning in the process industries; Video and image-based soft-sensors; Mobile and cloud computing in the industry; and Routine and predictive maintenance.
Last updated by Dou Sun in 2020-08-25
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