Journal Information
Control Engineering Practice
http://www.journals.elsevier.com/control-engineering-practice/
Impact Factor:
2.616
Publisher:
ELSEVIER
ISSN:
0967-0661
Viewed:
5490
Tracked:
0

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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 2019-01-05
Special Issues
Special Issue on Industrial Cyber-Physical Systems, Industrial Artificial Intelligence and Their Applications
Submission Date: 2019-12-31

As an integration of computing, communication and control, Cyber-Physical Systems (CPS) has been coming into our view and widely applied in daily life, for example in transportation systems, automation, security, smart buildings, smart cities, medical systems, energy generation and distribution, water distribution, agriculture, military systems, process control, asset management, and robotics. With the development of Industry 4.0, it is expected to build Industrial Cyber-Physical Systems (ICPS), in which the methods and models are not only combined, but also as a system constitutes a new engineering discipline related to Artificial Intelligence that promotes the development of industry itself. ICPS has received sufficiently technical support from communication infrastructure, computing facilities and sensing techniques. While considering the emerging requirements on intelligent capability, the existing ICPS still needs to be combined with Artificial Intelligence (AI) so as to provide smart and automatic solutions to real-world industrial applications. Besides, it is also interesting and significant to unite the cyber and the physical in an AI-driven framework for applying to the industrial systems. The related research may address the following issues: Industrial time series prediction AI-driven industrial planning and scheduling Deep reinforcement learning Data-based industrial optimization; and Other AI technologies for industrial application The purpose of this special issue (SI) is to provide a forum for researchers and practitioners to exchange ideas and progress in related areas. It aims at highlighting ICPS, Industrial AI (IAI) and applications, so as to make the concepts more visible and practical in the AI community. Original contributions, including novel ICPS framework, industrial data preprocessing, AI-driven time series prediction, industrial scheduling and optimization, and real-world case studies on industrial applications, are cordially welcome. Based on this SI, some fundamental concepts and associated techniques for ICPS and IAI will be further focused and promoted. The topics of this special issue include, but are not limited to: New and powerful innovations that will satisfy the future challenging requirements of Industrial Cyber-Physical Systems Architectural framework for distributed Industrial Cyber-Physical Systems Design, implementation and operation of intelligent Industrial Cyber-Physical Systems The Internet of Things (IoT) in industry, including edge computing, smart gateways, etc. Safety, privacy and security for Industrial Cyber-Physical Systems Data abnormality detection, imputation and fault analysis for Industrial Cyber-Physical Systems Time-series prediction, and prediction intervals construction for industrial data AI-driven industrial scheduling, operational optimization and decision-making AI-driven approaches, e.g., Deep reinforcement learning, fuzzy modeling, etc., for industrial applications
Last updated by Dou Sun in 2019-09-07
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