This brief addresses the design of a Nonlinear Model Predictive Control
(NMPC) strategy for exponentially incremental Input-to-State Stable (ISS)
systems. In particular, a novel formulation is devised, which does not
necessitate the onerous computation of terminal ingredients, but rather relies
on the explicit definition of a minimum prediction horizon ensuring closed-loop
stability. The designed methodology is particularly suited for the control of
systems learned by Recurrent Neural Networks (RNNs), which are known for their
enhanced modeling capabilities and for which the incremental ISS properties can
be studied thanks to simple algebraic conditions. The approach is applied to
Gated Recurrent Unit (GRU) networks, providing also a method for the design of
a tailored state observer with convergence guarantees. The resulting control
architecture is tested on a benchmark system, demonstrating its good control
performances and efficient applicability.

设计了一种新颖的非线性模型预测控制策略，适用于具有增量输入输出稳定性的系统，特别适用于递归神经网络学习的系统，并应用于门控循环单元网络的控制架构，通过基于简单的代数条件实现闭环稳定和状态观察器设计。

增量 ISS 系统的非线性 MPC 设计及应用于 GRU 网络

Nonlinear MPC design for incrementally ISS systems with application to  GRU networks

The imitation learning research community has recently made significant
progress towards the goal of enabling artificial agents to imitate behaviors
from video demonstrations alone. However, current state-of-the-art approaches
developed for this problem exhibit high sample complexity due, in part, to the
high-dimensional nature of video observations. Towards addressing this issue,
we introduce here a new algorithm called Visual Generative Adversarial
Imitation from Observation using a State Observer VGAIfO-SO. At its core,
VGAIfO-SO seeks to address sample inefficiency using a novel, self-supervised
state observer, which provides estimates of lower-dimensional proprioceptive
state representations from high-dimensional images. We show experimentally in
several continuous control environments that VGAIfO-SO is more sample efficient
than other IfO algorithms at learning from video-only demonstrations and can
sometimes even achieve performance close to the Generative Adversarial
Imitation from Observation (GAIfO) algorithm that has privileged access to the
demonstrator's proprioceptive state information.

介绍了一种名为 Visual Generative Adversarial Imitation from Observation using a State Observer (VGAIfO-SO) 的新算法，该算法使用自我监督的状态观察器从高维图像中提供低维本体感知状态表示的估计，从而更有效地从仅视频演示中学习，并且有时可以实现接近于有特权访问演示者本体感知状态信息的 GAIfO 算法的性能。