征文与专刊  
Energies 2019能源效率与数据驱动控制专刊征文

Energy专刊"Energy Efficiency and Data-Driven Control"开始征稿,本次专刊由侯忠生教授和Precup教授担任客座编辑。截稿时间为2019年3月25日。欢迎专家学者们投稿!专刊的详细信息请见链接:http://www.mdpi.com/journal/energies/special_issues/data_driven_control

Special Issue Information

Dear Colleagues,

The last decade has lead to a serious step forward regarding the complexity of processes, and also to high demanding performance, including energy efficiency. Advanced control systems that include intelligent control, adaptive control, data-driven and learning control, have been successfully applied to cope with the uncertainties and disturbances of many processes. The optimization algorithms play an important role in this context as they give, in case of correct formulations, solutions to rather complicated problems in order to meet systematically the performance specifications of control systems.

Nowadays, process control applications are developed in the conditions of optimal performance requirements. However, there is generally no dynamical model available for the process, or the process model is too complex to be used in controller design. Since modeling and system identification tools can be expensive and time-consuming, and models may be time-varying, or nonlinear, or contain delays, data-driven control has been proposed, with the aim to avoid the use of process models in controller tuning and to efficiently use the information in large amounts of process input–output data to design predictors, controllers, and monitoring systems that guarantee the required control system performance.

Energy efficiency deals with hot topics related to energy efficiency, energy savings, energy consumption, energy sufficiency, and energy transition. Since efficiency requires adequate performance indices to define and assess, the intersection of energy efficiency and data-driven control leads to high control system performance. Nevertheless, model-free versus model-based tuning problems have to be treated carefully.

The main objective of this Special Issue is to create a platform for scientists, engineers and practitioners to share their latest theoretical and technological results and to discuss several issues in the research directions of the fields of energy efficiency and data-driven control. The papers to be published in this Special Issue are expected to provide recent results in advanced controller design and tuning techniques, especially for cross-fertilizations between the fields of energy efficiency and data-driven control. Papers containing experimental results regarding advanced control systems and optimization are especially welcome.

Prof. Radu-Emil Precup

Prof. Zhongsheng Hou

Guest Editors

 

Keywords

Data-driven control, monitoring and modeling

Data-driven optimization, scheduling, decision and simulation

Data-driven fault diagnosis and performance evaluation

Model-free control

Model-free adaptive control

Iterative learning control and identification

Advanced intelligent techniques for data-driven control and optimization

Active disturbance rejection control

Learning-based control

Reinforcement learning for real-time control and optimization

Approximate dynamic programming

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中国自动化学会
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过程控制专业委员会
仪表与装置专业委员会
电气自动化专业委员会
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北京交通大学先进控制系统研究所
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