基于神经网络的经常性神经网络内部模型控制未知非线性稳定系统 (Recurrent neural network-based Internal Model Control of unknown nonlinear stable systems)

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Owing to their superior modeling capabilities, gated Recurrent Neural Networks (RNNs), 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, a first gated RNN is used to learn a model of the unknown input-output stable plant. Then, another gated RNN approximating the model inverse is trained. The proposed scheme is able to cope with the saturation of the control variables, and it can be deployed on low-power embedded controllers since it does not require any online computation. The approach is then tested on the Quadruple Tank benchmark system, resulting in satisfactory closed-loop performances.

翻译：由于其先进的模型能力,Gated Official Committees(GRUs)和长短期内存网络(LSTMs)等封闭式经常性神经网络(RNN)已成为学习动态系统的流行工具,本文件旨在讨论如何将这些网络用于综合内部模型控制(IMC)结构。为此,第一个封闭式RNN用于学习未知输入-输出稳定厂的模型。随后,又培训了另一个封闭式RNN(GRUs)和长短期内存网络(LSTMs),以适应控制变量的饱和,并可以部署在低功率嵌入控制器上,因为不需要任何在线计算。然后在四轮式坦克基准系统中测试该方法,从而产生令人满意的闭环性能。