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System Identification I

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The article was published on 2012-12-11. It has received 1704 citations till now. The article focuses on the topics: Nonlinear system identification & System identification.

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Dynamic Neural Network-Based Adaptive Tracking Control for an Autonomous Underwater Vehicle Subject to Modeling and Parametric Uncertainties

TL;DR: An identification-control scheme for each dynamic named Dynamic Neural Control System (DNCS) is proposed as a combination of an adaptive neural controller based on nonparametric identification of the effect of unknown dynamics and external disturbances, and on parametric estimation of the added mass dependent input gain.
Posted Content

Sample Complexity of Sparse System Identification Problem

TL;DR: A sparsity promoting block-regularized estimator to identify the dynamics of the system with only a limited number of input-state data samples is proposed, and it is shown that this estimator results in a small element-wise error, provided that the number of sample trajectories is above a threshold.
Journal ArticleDOI

Heavy vehicle suspension parameters identification and estimation of vertical forces: experimental results

TL;DR: Suspension stiffness and unsprung masses have been identified and Experimental results carried out on an instrumented tractor have been presented in order to show the quality of the state observation, parameters identification and force estimation.
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Retrofit control with approximate environment modeling

TL;DR: In this article, the authors developed a retrofit control method with approximate environment modeling, which is a modular control approach for a general stable network system whose subsystems are supposed to be managed by their corresponding subsystem operators.

Scramjet isolator modeling and control

TL;DR: In this article, the authors used shadowgraph images to measure the shock train leading edge (LE) position with root mean square (RMS) errors less than 20% of a duct height.
References
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Book

System Identification: Theory for the User

Lennart Ljung
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
Journal ArticleDOI

Deep learning in neural networks

TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
Journal ArticleDOI

Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control

TL;DR: This work extends the Koopman operator to controlled dynamical systems and applies the Extended Dynamic Mode Decomposition (EDMD) to compute a finite-dimensional approximation of the operator in such a way that this approximation has the form of a linearcontrolled dynamical system.
Journal ArticleDOI

A Tour of Reinforcement Learning: The View from Continuous Control

TL;DR: The authors surveys reinforcement learning from the perspective of optimization and control, with a focus on continuous control applications, and reviews the general formulation, terminology, and techniques for reinforcement learning for continuous control.
Journal ArticleDOI

SPICE: A Sparse Covariance-Based Estimation Method for Array Processing

TL;DR: This paper presents a novel SParse Iterative Covariance-based Estimation approach, abbreviated as SPICE, to array processing, obtained by the minimization of a covariance matrix fitting criterion and is particularly useful in many- snapshot cases but can be used even in single-snapshot situations.