Behavioral systems theory in data-driven analysis, signal processing, and control
Ivan Markovsky,Florian Dörfler +1 more
TLDR
Data-driven analysis, signal processing, and control methods as mentioned in this paper can be broadly classified as implicit and explicit approaches, with the implicit approach being more robust to uncertainty and robustness to noise.About:
This article is published in Annual Reviews in Control.The article was published on 2021-11-10 and is currently open access. It has received 38 citations till now. The article focuses on the topics: Robust control & Model predictive control.read more
Citations
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Proceedings ArticleDOI
Fusion of Machine Learning and MPC under Uncertainty: What Advances Are on the Horizon?
TL;DR: In this article , the authors provide an overview of the recent research efforts on the integration of machine learning and model predictive control under uncertainty, and present a collection of four major categories: learning models from system data and prior knowledge; learning control policy parameters from closed-loop performance data; learning efficient approximations of iterative online optimization from policy data; and learning optimal cost-to-go representations from closed loop performance data.
Proceedings ArticleDOI
Data-Driven Prediction with Stochastic Data: Confidence Regions and Minimum Mean-Squared Error Estimates
TL;DR: In this paper , confidence regions are provided for these stochastic pre-dictors based on the prediction error distribution, and an optimal predictor which achieves minimum mean-squared prediction error is also proposed to enhance prediction accuracy.
Posted Content
Design of input for data-driven simulation with Hankel and Page matrices.
TL;DR: In this paper, the problem of designing informative input trajectories for data-driven simulation is considered and a Bayesian interpretation is provided, and the implications of using Hankel and Page matrix representations are demonstrated.
Proceedings ArticleDOI
Behavioral Feedback for Optimal LQG Control
TL;DR: In this paper , the optimal LQG controller can be expressed as a static behavioral-feedback gain, thereby eliminating the need for dynamic state estimation characteristic of state space methods and making it amenable to its computation by gradient descent, which is investigated via numerical experiments.
Journal ArticleDOI
Identifiability in the Behavioral Setting
TL;DR: In this article , the authors derived necessary and sufficient identifiability conditions for deterministic linear time-invariant systems that do not require a priori given input/output partitioning of the variables nor controllability of the true system.
References
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Journal ArticleDOI
Time Series Analysis: Forecasting and Control
TL;DR: Time Series Analysis and Forecasting: principles and practice as mentioned in this paper The Oxford Handbook of Quantitative Methods, Vol. 3, No. 2: Statistical AnalysisTime-Series ForecastingPractical Time-Series AnalysisApplied Bayesian Forecasting and Time Series AnalysisSAS for Forecasting Time SeriesApplied Time Series analysisTime Series analysisElements of Nonlinear Time Series analyses and forecastingTime series analysis and forecasting by Example.
Journal ArticleDOI
Model predictive control: theory and practice—a survey
TL;DR: The flexible constraint handling capabilities of MPC are shown to be a significant advantage in the context of the overall operating objectives of the process industries and the 1-, 2-, and ∞-norm formulations of the performance objective are discussed.
Book
Proximal Algorithms
Neal Parikh,Stephen Boyd +1 more
TL;DR: The many different interpretations of proximal operators and algorithms are discussed, their connections to many other topics in optimization and applied mathematics are described, some popular algorithms are surveyed, and a large number of examples of proxiesimal operators that commonly arise in practice are provided.
Book
Optimal Control: Linear Quadratic Methods
TL;DR: In this article, an augmented edition of a respected text teaches the reader how to use linear quadratic Gaussian methods effectively for the design of control systems, with step-by-step explanations that show clearly how to make practical use of the material.