Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Composing MPC with LQR and Neural Networks for Efficient and Stable
  Control   Model predictive control (MPC) is a powerful control method that handles
dynamical systems with constraints. However, solving MPC iteratively in real
time, i.e., implicit MPC, has been a challenge for 1) systems with low-latency
requirements, 2) systems with limited computational resources, and 3) systems
with fast and complex dynamics. To address this challenge, for low-dimensional
linear systems, a classical approach is explicit MPC; for high-dimensional or
nonlinear systems, a common approach is function approximation using neural
networks. Both methods, whenever applicable, may improve the computational
efficiency of the original MPC by several orders of magnitude. The existing
methods have the following disadvantages: 1) explicit MPC does not apply to
higher-dimensional problems or most of the problems with nonlinear constraints;
and 2) function approximation is not guaranteed to find an accurate surrogate
policy, the failure of which may lead to closed-loop instability. To address
these issues, we propose a triple-mode hybrid control scheme, named
Memory-Augmented MPC, by combining an efficient linear quadratic regulator, an
efficient neural network, and a costly, fail-safe MPC. From its standard form,
we derive two variants of such hybrid control scheme: one customized for
chaotic systems and the other for slow systems. We prove stability of the
control scheme with any arbitrary neural networks and test its computational
performance in simulated numerical experiments.

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The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
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The categories of this paper are: bayes-an
16
The categories of this paper are: stat
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The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
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The categories of this paper are: hep-ph
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The categories of this paper are: physics
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-ex
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The categories of this paper are: math
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The categories of this paper are: funct-an
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The categories of this paper are: quant-ph