Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Hybrid Gaussian Process Approach to Robust Economic Model Predictive
  Control   This paper proposes a hybrid Gaussian process (GP) approach to robust
economic model predictive control under unknown future disturbances in order to
reduce the conservatism of the controller. The proposed hybrid GP is a
combination of two well-known methods, namely, kernel composition and nonlinear
auto-regressive. A switching mechanism is employed to select one of these
methods for disturbance prediction after analyzing the prediction outcomes. The
hybrid GP is intended to detect not only patterns but also unexpected behaviors
in the unknown disturbances by using past disturbance measurements.
\textcolor{black}{A novel forgetting factor concept is also utilized in the
hybrid GP, giving less weight to older measurements, in order to increase
prediction accuracy based on recent disturbances values.} The detected
disturbance information is used to reduce prediction uncertainty in economic
model predictive controllers systematically. The simulation results show that
the proposed method can improve the overall performance of an economic model
predictive controller compared to other GP-based methods in cases when
disturbances have discernible patterns.

2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin