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 Benchmark Control Applications
截稿日期: 2025-10-31

Control engineering continues to evolve in a wide range of theoretical and applied directions. The recent resurgence of interest in machine learning algorithms and their intersection with control engineering has led to an explosion of algorithms and applications. This has made it difficult to benchmark and compare algorithms. Moreover, new control algorithms developed by researchers are often tested on small and illustrative but simplified numerical application examples, limiting their practical relevance for practicing control engineers and making comparisons with state-of-the-art methods difficult. Indeed, representative benchmarks and models are paramount to design and evaluate new model- and data-based controllers and to optimize them before porting them to the practical application. Finally, the systems and control community rarely shares code, making reproduction of algorithms a time consuming task.

The objective of this Special Issue is to collect a set of challenging benchmark control applications that are high-fidelity enough to be relevant for practical/industrial applications and are suitable for the control research community. These benchmarks will include reference control/system identification methods for comparative analysis.

Potential domains of interest include, but are not limited to:

    Industrial Processes
    Aviation and Space
    Automotive
    Power and Energy systems
    Mechatronic Systems

Guest editors:

Laurent Burlion (Executive Guest Editor), Aerospace control, Rutgers University, USA (contact: Laurent.burlion@rutgers.edu)
Lars Eriksson, Automotive control, Linköping University, Sweden (contact: lars.eriksson@liu.se)
Marco Forgione, Identification/Estimation, Dalle Molle Institute for Artificial Intelligence, Switzerland (contact: marco.forgione@supsi.ch)
Bhushan Golupani, Industrial process control/data analytics /machine learning, the University of British Columbia, Canada (contact: bhushan.gopaluni@ubc.ca)
Peter Fogh Odgaard, Power systems, Goldwind Energy, Denmark (contact: odgaard@ieee.org)
Maarten Schoukens, Identification/Machine learning modeling, Eindhoven University of Technology, Netherlands (contact: m.schoukens@tue.nl)

Special issue information:

We aim for papers in this Special Issue to provide benchmarks in various formats:

• Open-Source Simulators: Fully accessible simulators (ideally in MATLAB or Python) that can be downloaded and used to implement and test control algorithms. The underlying system dynamics may be openly available and known or included as executables and thereby hidden with unknown structure and parameters. In the latter case users can for example use the executable to generate data for system identification or other training methods in the control synthesis and evaluation.

• Model Equations with Validated Data: The dynamic equations are provided along with numerical parameter values, enabling readers to construct the benchmark in their preferred language. A reference implementation may be provided to validate that the users implemented the model correctly.

• Input/Output Trajectories: The simulator or experimental setup is not available but input/output data has been collected and can be used for system identification or machine learning modeling. This data can be divided into training and test datasets.

• Remotely Accessible Simulators or Real-Time Experiments: Remote access to realtime experimental setups or complex numerical simulators under specified conditions.

Researchers can implement and submit their controllers, and receive the resulting data. Again, the system dynamics may be known or unknown, but input-output data will be available after a submitted and executed test in the latter case.

For each benchmark, it is desirable to include:

• A nominal solution: A baseline, legacy, state-of-the-art control (or system identification) method for comparison purposes.

• Clearly defined tasks and performance criteria: Specific metrics to evaluate the performance of different algorithms for a given task. This includes a description of the known challenges that are present in the task at hand for the considered system.

• Result interpretation: Post-processing tools in the form of additional functions or scripts can be provided to facilitate analysis and visualization of simulation or experimental results. Furthermore, additional guidelines in terms of the qualitative description of the results (e.g. computing platform and time), can be provided to be able to interpret the results beyond the provided performance criteria.

• Standardized data and simulation environments: Provide the data and system simulators in a well-documented way, following good practices and standardized formats, allowing for easy accessibility of the benchmark to the users.

By providing these comprehensive benchmark applications, we hope to stimulate openness and innovation in control research that can promote the development of more practical and effective control algorithms, that practicing engineers can use as inspiration and adopt in their work flow.

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: Benchmark Control Applications”. 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 copyrighted 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.

Control Engineering Practice publishes 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. In this light, simulation models for the benchmarks in this special issue must be representative for and validated at the real plant under consideration. The interested authors are encouraged to read other CEP papers in the similar field to learn more about CEP’s standards and relevance to your work.

Important Dates

• Submission deadline: October 31, 2025
• Acceptance deadline: May 31, 2026

Keywords:

benchmark; high-fidelity; model-based control; data-based control; system identification