Journal ArticleDOI
From model-based control to data-driven control: Survey, classification and perspective
Zhongsheng Hou,Zhuo Wang +1 more
TLDR
This paper is a brief survey on the existing problems and challenges inherent in model-based control (MBC) theory, and some important issues in the analysis and design of data-driven control (DDC) methods are here reviewed and addressed.About:
This article is published in Information Sciences.The article was published on 2013-06-01. It has received 828 citations till now.read more
Citations
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Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Journal ArticleDOI
Data-Based Techniques Focused on Modern Industry: An Overview
TL;DR: The main objective of this paper is to review and summarize the recent achievements in data-based techniques, especially for complicated industrial applications, thus providing a referee for further study on the related topics both from academic and practical points of view.
Proceedings ArticleDOI
Data-Enabled Predictive Control: In the Shallows of the DeePC
TL;DR: In this paper, a data-enabled predictive control (DeePC) algorithm is presented that computes optimal and safe control policies using real-time feedback driving the unknown system along a desired trajectory while satisfying system constraints.
Big Data for Modern Industry: Challenges and Trends
Shen Yin,Okyay Kaynak +1 more
TL;DR: Finds ways in business and industry is working to manage data and reports on applications that support these initiatives.
Journal ArticleDOI
Data-Driven Model Predictive Control With Stability and Robustness Guarantees
TL;DR: The presented results provide the first (theoretical) analysis of closed-loop properties, resulting from a simple, purely data-driven MPC scheme, including a slack variable with regularization in the cost.
References
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Journal ArticleDOI
Virtual reference direct design method: an off-line approach to data-based control system design
TL;DR: This paper presents a direct model-reference approach to the off-line design of linear controllers, suited to deal with plants described by a single set of open-loop I/O measurements only, which reduces to a standard identification problem.
Proceedings ArticleDOI
The model-free learning adaptive control of a class of SISO nonlinear systems
Zhongsheng Hou,Wenhu Huang +1 more
TL;DR: In this article, the model-free learning adaptive control of a class of nonlinear systems is presented based on a new concept of partial derivative called pseudo-partial derivative called "pseudo-partial-derivative".
Journal Article
On Data-driven Control Theory:the State of the Art and Perspective
TL;DR: The state-of-art of the existing data-driven control methods are presented with appropriate classifications and insights, and the differences among these methods and the application scopes are also highlighted.
Journal ArticleDOI
Challenges of Adaptive Control-Past, Permanent and Future
TL;DR: Three different types of challenges to adaptive control are reviewed, including difficulties associated with the MIT rule, bursting, the Rohr’s counterexample and unplanned instability in iterative identification and control.
Journal ArticleDOI
An iterative learning approach for density control of freeway traffic flow via ramp metering
TL;DR: The iterative learning control approach is applied to address the traffic density control problem in a macroscopic level freeway environment with ramp metering to effectively deal with this class of control problem and greatly improve the traffic response.
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