Bayesian Nonparametric Adaptive Control using Gaussian Processes
Name
TNN12.pdf
Description
Main article
Size
2.49 MB
Format
Adobe PDF
Checksum (MD5)
33dbef62713819afe91d29f9f3d4bdd3
Author(s)
Chowdhary, Girish
Kingravi, Hassan A.
How, Jonathan P.
Vela, Patricio A.
Date Issued
March 15, 2013
Abstract
Most current Model Reference Adaptive Control
(MRAC) methods rely on parametric adaptive elements, in
which the number of parameters of the adaptive element are
fixed a priori, often through expert judgment. An example of
such an adaptive element are Radial Basis Function Networks
(RBFNs), with RBF centers pre-allocated based on the expected
operating domain. If the system operates outside of the expected
operating domain, this adaptive element can become
non-effective in capturing and canceling the uncertainty, thus
rendering the adaptive controller only semi-global in nature.
This paper investigates a Gaussian Process (GP) based Bayesian
MRAC architecture (GP-MRAC), which leverages the power and
flexibility of GP Bayesian nonparametric models of uncertainty.
GP-MRAC does not require the centers to be preallocated, can
inherently handle measurement noise, and enables MRAC to
handle a broader set of uncertainties, including those that are
defined as distributions over functions. We use stochastic stability
arguments to show that GP-MRAC guarantees good closed loop
performance with no prior domain knowledge of the uncertainty.
Online implementable GP inference methods are compared in
numerical simulations against RBFN-MRAC with preallocated
centers and are shown to provide better tracking and improved
long-term learning.
Description
This technical report is a preprint of an article submitted to a journal.
Subjects
kernel machines
adaptive control
gaussian processes
Bayesian Nonparametric Models
Terms of Use
Attribution-NonCommercial-ShareAlike 3.0 United States
Persistent DSpace Link
