Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reducing the computational effort of min-max model predictive control
  with regional feedback laws   Recently, a regional MPC approach has been proposed that exploits the
piecewise affine structure of the optimal solution (without computing the
entire explicit solution before). Here, regional refers to the idea of using
the affine feedback law that is optimal in a vicinity of the current state of
operation, and therefore provides the optimal input signal without requiring to
solve a QP. In the present paper, we apply the idea of regional MPC to min-max
MPC problems. We show that the new robust approach can significantly reduce the
number of QPs to be solved within min-max MPC resulting in a reduced overall
computational effort. Moreover, we compare the performance of the new approach
to an existing robust regional MPC approach using a numerical example with
varying horizon. Finally, we provide a rule for choosing a suitable robust
regional MPC approach based on the horizon.

4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
1
The categories of this paper are: cs
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin