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Adaptive-optimal control under time-varying stochastic uncertainty using past learning
Ali Abdollahi
,
Girish Chowdhary
Agricultural and Biological Engineering
Electrical and Computer Engineering
Aerospace Engineering
Coordinated Science Lab
National Center for Supercomputing Applications (NCSA)
Carl R. Woese Institute for Genomic Biology
Siebel School of Computing and Data Science
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peer-review
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Keyphrases
Model Reference Adaptive System
100%
Stochastic Uncertainty
100%
Adaptive Optimal Control
100%
Process Clustering
66%
Online Gaussian Process
66%
Process-based
33%
Adaptive Control
33%
Similarity Model
33%
Control Architecture
33%
Controller
33%
Clustering Algorithm
33%
Excitation Condition
33%
Dynamic Stochastic
33%
Gaussian Process
33%
Unmatched Uncertainties
33%
Model Predictive Control
33%
Persistent Excitation
33%
Likelihood Ratio Test
33%
Wing Rock
33%
Non-Bayesian
33%
Gaussian Process Model
33%
Bayesian Nonparametric Model
33%
Learning Transient
33%
Aerospace Systems
33%
Bayesian Clustering
33%
Rock Dynamics
33%
Command Shaping
33%
Predictive Model
33%
Computer Science
Adaptive Control Systems
100%
Optimal Control
100%
Reference Model
75%
Predictive Model
50%
clustering process
50%
Likelihood Ratio
25%
Clustering Algorithm
25%
gaussian process model
25%