Book ChapterDOI
System Identification I
Biao Huang,Yutong Qi,Akm Monjur Murshed +2 more
- pp 31-56
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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.read more
Citations
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Journal ArticleDOI
Driver-Automation Cooperation Oriented Approach for Shared Control of Lane Keeping Assist Systems
TL;DR: The core idea is to combine system perception with robust control so that the proposed strategy can successfully share the control authority between human drivers and the LKA system.
Journal ArticleDOI
System Identification for Small, Low-Cost, Fixed-Wing Unmanned Aircraft
TL;DR: In this article, a linear model, obtained from the generic nonlinear equations of motion for aircraft, is used as a basis for system identification, and the parameters of the linear model are identified by fitting the model to frequency responses extracted from the data.
Proceedings Article
Improper Learning for Non-Stochastic Control
TL;DR: Borders are the first in the non-stochastic control setting that compete with \emph{all} stabilizing linear dynamical controllers, not just state feedback, and applies in a more general setting with adversarial losses and semi-adversarial noise.
Journal ArticleDOI
Machine learning for fast and reliable solution of time-dependent differential equations
TL;DR: It is proved that ANN models are able to approximate every time-dependent model described by ODEs with any desired level of accuracy, and is tested on different problems, including the model reduction of two large-scale models.
Journal ArticleDOI
Imitation learning for agile autonomous driving
Yunpeng Pan,Ching-An Cheng,Kamil Saigol,Keuntaek Lee,Xinyan Yan,Evangelos A. Theodorou,Byron Boots +6 more
TL;DR: This work presents an end-to-end imitation learning system for agile, off-road autonomous driving using only low-cost on-board sensors and shows that policies trained with online imitation learning overcome well-known challenges related to covariate shift and generalize better than policiestrained with batch imitation learning.
References
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Book
System Identification: Theory for the User
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
Milan Korda,Igor Mezic +1 more
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
Petre Stoica,Prabhu Babu,Jian Li +2 more
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.