Guest Editorial [Special Issue: Data-driven Control, and Data-Based System Modelling, Monitoring, and Control]
In modern industrial processes, aerospace systems, vehicle systems, and elsewhere there are increased demands for fuel efficiency, conservation of resources, cost and energy savings, and other optimal performance requirements. However, there is generally no dynamical model available for the process, or the process model is too complex to be tractable for controller design. Modelling and system identification are expensive and time-consuming, and models may be time-varying, or non-linear, or contain delays. The term ‘Data-driven Control’ (DDC) originated in the 1990s in Computer Science and it shares the same context as ‘big data’, ‘data mining’, and ‘data fusion’. On the other hand, Data-Based System Modelling, Monitoring, and Control are a set of topics used in the Control Systems community. The development of all these topics was driven by the huge amounts of data measured in complex process control systems, both stored historical data from prior measurements and on-line data available in real time during process runs. In these fields, the intent is to efficiently use the information in huge amounts of process input/output data to design predictors, controllers, and monitoring systems that provide guaranteed performance of the process.
This Special Issue presents the latest developments on datadriven modelling and control, iterative learning control and reinforcement learning, and their applications in process industries. It contains eighteen papers…
J. Ding et al., "Guest Editorial [Special Issue: Data-driven Control, and Data-Based System Modelling, Monitoring, and Control]," IET Control Theory and Applications, vol. 10, no. 12, pp. 1319-1321, Institution of Engineering and Technology, Jul 2016.
The definitive version is available at https://doi.org/10.1049/iet-cta.2016.0646
Electrical and Computer Engineering
International Standard Serial Number (ISSN)
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