Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Identifiability Implies Robust, Globally Exponentially Convergent
  On-line Parameter Estimation: Application to Model Reference Adaptive Control   In this paper we propose a new parameter estimator that ensures global
exponential convergence of linear regression models requiring only the
necessary assumption of identifiability of the regression equation,which we
show is equivalent to interval excitation of the regressor vector. Continuous
and discrete-time versions of the estimators are given. An extension
to--separable and monotonic--non-linear parameterizations is also given. The
estimators are shown to be robust to additive measurement noise and--not
necessarily slow--parameter variations. Moreover, a version of the
continuous-time estimator that rejects sinusoidal disturbances with unknown
internal model is given. The estimator is shown to be applicable to the
classical model reference adaptive control problem relaxing the conspicuous
assumption of known sign of the high-frequency gain. Simulation results that
illustrate the performance of the estimator are given.

20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an