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L
1
-GP: L
1
Adaptive Control with Bayesian Learning
Aditya Gahlawat
, Pan Zhao
, Andrew Patterson
,
Naira Hovakimyan
, Evangelos A. Theodorou
Mechanical Science and Engineering
Siebel School of Computing and Data Science
Electrical and Computer Engineering
Aerospace Engineering
Information Trust Institute
Coordinated Science Lab
Beckman Institute for Advanced Science and Technology
National Center for Supercomputing Applications (NCSA)
Research output
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Contribution to journal
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Conference article
›
peer-review
Overview
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Dive into the research topics of 'L
1
-GP: L
1
Adaptive Control with Bayesian Learning'. Together they form a unique fingerprint.
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Keyphrases
Adaptive Control Strategy
33%
Architecture-centric
33%
Bayesian Learning
100%
Conservative Design
33%
Control Architecture
33%
Design for Performance
33%
Gaussian Process Regression
66%
L1 Adaptive Controller
100%
Less Conservative
33%
Numerical Simulation
33%
Performance Guarantee
33%
Performance Robustness
33%
Proposed Architecture
33%
Robust Performance
33%
Simultaneous Control
33%
Simultaneous Learning
33%
Stability Performance
33%
Tracking Performance
33%
Transient Performance
33%
Uncertain Dynamics
33%
Engineering
Adaptive Control
100%
Control Architecture
33%
Gaussians
66%
Illustrates
33%
Transients
33%
Computer Science
Adaptive Control Systems
100%
Bayesian Learning
100%
Numerical Simulation
33%
Performance Guarantee
33%
Chemical Engineering
Adaptive Control Systems
100%