Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Output Feedback Control Based on State and Disturbance Estimation   Recently developed control methods with strong disturbance rejection
capabilities provide a useful option for control design. The key lies in a
general concept of disturbance and effective ways to estimate and compensate
the disturbance. This work extends the concept of disturbance as the mismatch
between a system model and the true dynamics, and estimates and compensates the
disturbance for multi-input multi-output linear/nonlinear systems described in
a general form. The results presented do not need to assume the disturbance to
be independent of the control inputs or satisfy a certain matching condition,
and do not require the system to be expressible in an integral canonical form
as required by algorithms previously described in literature. The estimator and
controller are designed under a state tracking framework, and sufficient
conditions for the closed-loop stability are presented. The performance of the
resulting controller relies on a co-design of the system model, the state and
disturbance observer, and the controller. Numerical experiments on a
first-order system and an inverted pendulum under uncertainties are used to
illustrate the control design method and demonstrate its efficacy.

17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
1
The categories of this paper are: cs
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an