Dear Colleagues,

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

At present, 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

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• 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