GRU神经网络学习系统的非线性MPC无偏跟踪
Nonlinear MPC for Offset-Free Tracking of systems learned by GRU Neural Networks
摘要：
递归神经网络（Recurrent Neural Networks，RNNs）具有黑箱建模能力，近年来在系统辨识中的应用越来越受到人们的关注，尽管RNNs在许多应用中得到了卓有成效的应用，但只有很少的工作致力于提供严格的理论基础来证明其用于控制目的的合理性。本文的目的是描述如何稳定的门控递归单元（GRU），一个特定的RNN架构，可以训练和采用非线性MPC框架，以执行无偏移跟踪恒定参考与保证闭环稳定性。所提出的方法进行了测试的pH值中和过程的基准，表现出显着的性能。
英文摘要：
The use of Recurrent Neural Networks (RNNs) for system identification has recently gathered increasing attention, thanks to their black-box modeling capabilities.Albeit RNNs have been fruitfully adopted in many applications, only few works are devoted to provide rigorous theoretical foundations that justify their use for control purposes. The aim of this paper is to describe how stable Gated Recurrent Units (GRUs), a particular RNN architecture, can be trained and employed in a Nonlinear MPC framework to perform offset-free tracking of constant references with guaranteed closed-loop stability. The proposed approach is tested on a pH neutralization process benchmark, showing remarkable performances.
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