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.

Special Issue on New Applications of Data-driven Performance Optimization and Safety Assessment for Large-scale Systems
提出日: 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