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 2022-01-29
Special Issues
Special Issue on Data-driven learning, control and monitoring for intelligent glucose management
Submission Date: 2022-08-30

Guest editors: Order of guest editors Dr. Dawei Shi, Beijing Institute of Technology, China, Dr. Marzia Cescon, University of Houston, USA, Prof. Chunhui Zhao, Zhejiang University, China, Dr. Eyal Dassau, Harvard University, USA, Prof. Steffen Leonhardt, RWTH Aachen University, Germany, Prof. Francis J. Doyle III, Harvard University, USA, Special issue information: SI proposal Glucose management is crucial in delaying and avoiding complications in diabetes, and it is also important for life support in intensive medicine scenarios. The primary aim for glucose management is to achieve euglycemic control (in terms of percentage time in the euglycemic range, average glucose levels, glucose variability metrics, etc.) without increasing the risk of hypoglycemia. Traditionally, glucose management is mainly performed based on clinical experience and can be viewed as a decision and control problem. With the technological development of continuous glucose monitoring, continuous subcutaneous insulin infusion and wearable sensing technology, it has become possible to close the loop of the treatment optimization in real time, making the role of control engineering and machine learning relevant and important in this area. Compelling research problems range from learning & identification of the glucose regulation process, to real-time glucose monitoring and closed-loop control, and to long-term treatment policy learning and adaptation. The key difficulties arise from the unbalanced data & asymmetric risks for hyperglycemia and hypoglycemia, the limited data resources, the inter/intra-subject variability for glucose management, the nonlinear characteristics of the glucose metabolism process and the safety requirements for glucose regulation. Most importantly, it is of primary concern how to make efficient use of the abundance of continuously monitored data for glucose and other physiological variables, in learning, control and monitoring to enable intelligent glucose management. This special issue aims at providing an interdisciplinary platform for researchers, practitioners and clinicians to present and discuss recent developments, trends and concerns on the key problems and challenges encountered in data-driven learning, control and monitoring for intelligent glucose management. Potential topics include, but are not limited to: · Data-driven control algorithms for artificial pancreas systems · Modeling & identification of the glucose metabolism · Learning-based dosage optimization for glucose management · Long-term controller adaptation in glucose control · State estimation and alarm system design for glucose monitoring · Novel continuous glucose sensing and data-driven calibration algorithms · Utilization of additional sensor data information for glucose management · Digital technology enabled behavioral change interventions CEP 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. 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. 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. 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.”
Last updated by Dou Sun in 2022-01-29
Special Issue on Advanced Topics in Aerial Robotics
Submission Date: 2022-10-31

Potential submission topics include, but are not limited to: - Vision-based path planning, guidance, navigation, and control under faulty conditions - Path planning, guidance, navigation, and control in GPS denied environment - Design, dynamic modeling, identification, and construction of novel aerial robot concepts including fixed-wing, VTOL, and convertible UAVs - Novel concepts in fault detection, estimation, and fault-tolerant control of aerial robots - Laboratory testbed design, application, and implementation in aerial robotics - Novel approaches to aggregate high-level and low-level control systems for autonomous flight management of aerial robots - Novel system and control designs for swing flexible payloads, collaborative tasks and missions - Novel algorithms and implementations in formation and swarm flight of aerial robots - Novel grasping and gripper mechanisms for payload capturing - Intelligent flight control algorithms and implementations for real-time learning - Novel learning and deep learning-based algorithms applied in the categorization of the environment, object/human/target detection, control, and distance estimation - Novel designs and technologies for collaborative human-robot’s interactions - Novel flight controllers based on evolutionary algorithms under different uncertainties - Issues related to energy minimization and increased range - Novel designs and technologies to enhance the controller efficiency and reduce computational complexity - Effective consideration of structural and input-output constraints in the design and implementation of flight controllers - Novel and robust flight control systems for aggressive maneuvers and application in severe atmospheric conditions and wind fields - Obstacle avoidance, navigation, guidance, and control of agile aerial robots in an uncertain dynamic environment - Novel control designs and implementations for auto-landing of aerial robots under unpredictable flight conditions.
Last updated by Dou Sun in 2022-05-22
Special Issue on Advances in Control and Optimization for Disturbance/Uncertainty Rejection of Intelligent Autonomous Systems
Submission Date: 2022-12-31

Intelligent autonomous systems are characterized by the response to unforeseeable events without manual intervention in a timely and rational manner. To achieve stronger capability and better performance of disturbance/uncertainty rejection control for intelligent autonomous systems, optimization and learning methods are gradually attracting increasing attention from researchers. This special issue of Control Engineering Practice (CEP) is launched to address the questions of why and how on the technical side: Why should the leading industrial practitioners in intelligent autonomous systems pay serious attention to advanced disturbance/uncertainty rejection methods? How to exploit the information/models/learning to estimate and reject disturbance more effectively? How is the new generation of disturbance rejection control technologies being formulated? What are their applications domains and what differences do they make? Finally, the target manuscripts for this special issue include, but are not limited to, the following topics: - Intelligent control methods of disturbance estimation and rejection for autonomous systems - Optimization for disturbance estimation and rejection control for autonomous systems - Disturbance and uncertainty modelling, estimation, and prediction with learning and other methods - Exploit disturbance and uncertainty information to improve goal-oriented performance in autonomous systems - Practical solutions of disturbance rejection control for autonomous systems of unmanned vehicles, robots, power, and other fields - Effective combinations of disturbance rejection control methods and learning methods.
Last updated by Dou Sun in 2022-05-22
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