With the increasing demands on system performance, production quality as well as economic operation, modern industrial systems and processes are becoming more and more complicated. The analytical model from the first principals may either extremely hard to obtain in practice or could contain large uncertainties. As a result, to control, monitor as well fault diagnosis of such complex processes is posing a great challenge due to the possible unavailability of sufficient quantitative knowledge about the process. In contrast to traditional model-based approaches, data-driven control, monitoring and fault accommodation make use of the information obtained from the available process measurements to describe various complex behaviors, and thus have formed an efficient alternative for control and monitoring issues with complex industrial applications. The IEEE IES Technical Committee on Data-Driven Control and Monitoring aims to provide a forum for researchers and practitioners to exchange their latest achievements and to identify critical issues and challenges for future investigation on control, monitoring, fault diagnosis and optimization with complex industrial applications.

Current research in the area of Data-Driven Control and Monitoring can be generally listed as follows:

• Data-driven modeling and system identification
• Data-driven control performance monitoring and assessment
• Data-driven controller design
• Stability and robustness issues for data-driven methods
• Data-driven plant-wide optimization
• Data-driven fault diagnosis, fault predictability analysis and prognosis
• Data-driven fault-tolerant control and fault management

The major tasks of the technical committee are to promote researches and to monitor trends in this area, and also provide a platform for “data-driven” people over the world to collaborate researches together.