From model-based control to data-driven control: Survey, classification and perspective

By Z. Hou, Zhuo Wang. Information Sciences, 2013

1,290 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/fbedbb1c9f22858aaeab8e0cfbfa96b9b60cf2b5

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In Control and Systems Engineering

Citations in the corpus

On Lyapunov functions and data-driven dissipativity (2017, background): “…(2016, 2017); Markovsky and Rapisarda (2008)), where the design of a controller is based on system data rather than models, which are in many real-life situations not available, (see Hou and Wang (2013) for a formal definition of data-driven control and a summary of approaches in the literature).”
Data-Based Techniques Focused on Modern Industry: An Overview (2015, background): “The system can be expressed by three representative dynamic linearization data models, with uniformly bounded PPD [17], [19].”
Data-Driven Model Predictive Control With Stability and Robustness Guarantees (2019, background): “, stability or constraint satisfaction of system variables [1], [2].”
Applications of reinforcement learning in energy systems (2021, background): “These can be categorized into three classes: white box, gray box, and data-driven (black box) models [29].”
From Noisy Data to Feedback Controllers: Nonconservative Design via a Matrix S-Lemma (2020, background): “Our account of previous work is therefore not exhaustive, but we note that additional references can be found in the survey [4].”
Big Data for Modern Industry: Challenges and Trends [Point of View] (2015, background): “Methods and algorithms focusing on data-based aspects like statistical analyses in business, management, and biomedicine [10–12], datadriven process monitoring/ prognostics [13], [14], and system control and optimization [15], [16] have been widely investigated in recent”
Data-Enabled Predictive Control: In the Shallows of the DeePC (2018, background): “As systems are becoming more complex and data is becoming more readily available, scientists and practitioners are beginning to bypass classical model-based techniques in favour of data-driven methods [1].”
On Model-Free Adaptive Control and Its Stability Analysis (2019, methodology): “DDC methods can be cataloged into two classes, one is the DDC methods with prespecified fixed controller structure and the other is without prespecified controller structure [11].”
An Overview of Dynamic-Linearization-Based Data-Driven Control and Applications (2017, methodology): “When the plant model is unavailable except for the online and offline data, the controller design and analysis would be completely different from MBC, and many works are of fundamental and challenging since very few systematic studies exist up to now for DDC [8].”
Data Informativity: A New Perspective on Data-Driven Analysis and Control (2019, background): “Additional noteworthy data-driven control problems include predictive control [20]–[22], model reference control [23], [24], and (intelligent) PID control [25], [26] (for more references and classifications of data-driven control techniques, we refer to the survey [27]).”