Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recurrent Neural Network-based Internal Model Control design for stable
  nonlinear systems   Owing to their superior modeling capabilities, gated Recurrent Neural
Networks, such as Gated Recurrent Units (GRUs) and Long Short-Term Memory
networks (LSTMs), have become popular tools for learning dynamical systems.
This paper aims to discuss how these networks can be adopted for the synthesis
of Internal Model Control (IMC) architectures. To this end, first a gated
recurrent network is used to learn a model of the unknown input-output stable
plant. Then, a controller gated recurrent network is trained to approximate the
model inverse. The stability of these networks, ensured by means of a suitable
training procedure, allows to guarantee the input-output closed-loop stability.
The proposed scheme is able to cope with the saturation of the control
variables, and can be deployed on low-power embedded controllers, as it
requires limited online computations. The approach is then tested on the
Quadruple Tank benchmark system and compared to alternative control laws,
resulting in remarkable closed-loop performances.

11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph