Subtopic Deep Dive
Data-Driven Control Methods
Research Guide
What is Data-Driven Control Methods?
Data-driven control methods design controllers directly from input-output data without explicit system models.
These methods include model-free adaptive control (Hou and Jin, 2010, 713 citations) and data-driven model predictive control (Berberich et al., 2020, 683 citations). Techniques leverage sparse identification (Brunton et al., 2016, 4187 citations) and Gaussian processes (Deisenroth et al., 2013, 640 citations). Over 10 key papers from 1999-2021 span foundational regression to recent stability guarantees.
Why It Matters
Data-driven control reduces modeling efforts for high-dimensional systems in robotics and industry 4.0, enabling faster deployment (Deisenroth et al., 2013). It provides stability guarantees for model predictive control using behavioral systems theory (Berberich et al., 2020). Learning-based MPC integrates safe data-driven techniques for real-time applications (Hewing et al., 2019). Model-free adaptive control handles nonlinear discrete-time systems without dynamics knowledge (Hou and Jin, 2010).
Key Research Challenges
Stability Guarantees
Ensuring closed-loop stability without identified models remains difficult for nonlinear systems. Berberich et al. (2020) address this via behavioral theory for LTI systems. Extensions to uncertain dynamics require robust trajectory data.
Robustness to Noise
Data corruption and outliers degrade controller performance in sparse identification. Brunton et al. (2016) use sparsity for nonlinear dynamics discovery. Noise handling demands advanced regularization like least angle regression (Efron et al., 2004).
Scalability to High Dimensions
High-dimensional data challenges model-free methods in real-time control. Gaussian processes offer data efficiency but scale poorly (Deisenroth et al., 2013). Joint graphical lasso aids multi-class covariance estimation (Danaher et al., 2013).
Essential Papers
Least angle regression
Bradley Efron, Trevor Hastie, Iain M. Johnstone et al. · 2004 · The Annals of Statistics · 9.4K citations
The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be...
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Steven L. Brunton, Joshua L. Proctor, J. Nathan Kutz · 2016 · Proceedings of the National Academy of Sciences · 4.2K citations
Significance Understanding dynamic constraints and balances in nature has facilitated rapid development of knowledge and enabled technology, including aircraft, combustion engines, satellites, and ...
Review on model predictive control: an engineering perspective
Max Schwenzer, Muzaffer Ay, Thomas Bergs et al. · 2021 · The International Journal of Advanced Manufacturing Technology · 887 citations
Abstract Model-based predictive control (MPC) describes a set of advanced control methods, which make use of a process model to predict the future behavior of the controlled system. By solving a—po...
The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
Patrick Danaher, Pei Wang, Daniela Witten · 2013 · Journal of the Royal Statistical Society Series B (Statistical Methodology) · 883 citations
Summary We consider the problem of estimating multiple related Gaussian graphical models from a high dimensional data set with observations belonging to distinct classes. We propose the joint graph...
Learning-Based Model Predictive Control: Toward Safe Learning in Control
Lukas Hewing, Kim P. Wabersich, Marcel Menner et al. · 2019 · Annual Review of Control Robotics and Autonomous Systems · 716 citations
Recent successes in the field of machine learning, as well as the availability of increased sensing and computational capabilities in modern control systems, have led to a growing interest in learn...
A Novel Data-Driven Control Approach for a Class of Discrete-Time Nonlinear Systems
Zhongsheng Hou, Shangtai Jin · 2010 · IEEE Transactions on Control Systems Technology · 713 citations
In this work, a novel data-driven control approach, model-free adaptive control, is presented based on a new dynamic linearization technique for a class of discrete-time single-input and single-out...
Data-Driven Model Predictive Control With Stability and Robustness Guarantees
Julian Berberich, Johannes Köhler, Matthias A. Müller et al. · 2020 · IEEE Transactions on Automatic Control · 683 citations
We propose a robust data-driven model predictive control (MPC) scheme to control linear time-invariant (LTI) systems. The scheme uses an implicit model description based on behavioral systems theor...
Reading Guide
Foundational Papers
Start with Efron et al. (2004) for least angle regression in model selection, then Hou and Jin (2010) for model-free adaptive control, and Deisenroth et al. (2013) for Gaussian processes in robotics.
Recent Advances
Study Berberich et al. (2020) for data-driven MPC stability, Hewing et al. (2019) for learning-based safe control, and Brunton et al. (2016) for sparse dynamical systems.
Core Methods
Dynamic linearization (Hou and Jin, 2010), behavioral systems theory (Berberich et al., 2020), sparse identification (Brunton et al., 2016), Gaussian processes (Deisenroth et al., 2013).
How PapersFlow Helps You Research Data-Driven Control Methods
Discover & Search
Research Agent uses searchPapers and citationGraph to map data-driven control from Hou and Jin (2010) to recent works like Berberich et al. (2020). exaSearch uncovers stability-focused papers; findSimilarPapers extends Brunton et al. (2016) sparse methods to control applications.
Analyze & Verify
Analysis Agent applies readPaperContent to extract stability proofs from Berberich et al. (2020), then verifyResponse with CoVe checks claims against trajectories. runPythonAnalysis simulates Gaussian processes from Deisenroth et al. (2013) with NumPy; GRADE scores evidence on robustness guarantees.
Synthesize & Write
Synthesis Agent detects gaps in stability for nonlinear extensions beyond Hou and Jin (2010); Writing Agent uses latexEditText, latexSyncCitations for controller diagrams, and latexCompile for publication-ready manuscripts. exportMermaid visualizes data-driven MPC workflows.
Use Cases
"Reproduce model-free adaptive control simulation from Hou and Jin 2010"
Research Agent → searchPapers → Analysis Agent → readPaperContent + runPythonAnalysis (NumPy simulation of dynamic linearization) → matplotlib plots of convergence.
"Draft paper on data-driven MPC stability proofs"
Synthesis Agent → gap detection → Writing Agent → latexEditText (add Berberich et al. proofs) → latexSyncCitations → latexCompile → PDF with stability diagrams.
"Find GitHub code for sparse identification in control"
Research Agent → citationGraph (Brunton et al. 2016) → Code Discovery → paperExtractUrls → paperFindGithubRepo → githubRepoInspect → verified SINDy control implementations.
Automated Workflows
Deep Research workflow scans 50+ papers from Efron et al. (2004) to Hewing et al. (2019), producing structured reports on stability trends. DeepScan applies 7-step analysis with CoVe checkpoints to verify robustness in Berberich et al. (2020). Theorizer generates hypotheses linking sparse regression to nonlinear control gaps.
Frequently Asked Questions
What defines data-driven control methods?
Controller design directly from input-output data without explicit models, as in model-free adaptive control (Hou and Jin, 2010).
What are core methods?
Model-free adaptive control (Hou and Jin, 2010), sparse identification (Brunton et al., 2016), data-driven MPC (Berberich et al., 2020), and Gaussian processes (Deisenroth et al., 2013).
What are key papers?
Foundational: Efron et al. (2004, 9367 citations), Hou and Jin (2010, 713 citations); Recent: Brunton et al. (2016, 4187 citations), Berberich et al. (2020, 683 citations).
What are open problems?
Stability for nonlinear high-dimensional systems; robustness to noisy sparse data; scalable real-time learning beyond Gaussian processes.
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