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.

翻译：本文针对指数增量输入-状态稳定（ISS）系统，提出了非线性模型预测控制（NMPC）策略的设计方法。具体而言，本文设计了一种新颖的公式化方法，该方法无需计算繁重的终端约束，而是通过显式定义保证闭环稳定性的最小预测时域来实现。所提出的方法特别适用于由循环神经网络（RNN）学习得到的系统控制，这类网络以其强大的建模能力而闻名，且可通过简单的代数条件研究其增量ISS特性。该被应用于门控循环单元（GRU）网络，同时提供了一种具有收敛保证的定制状态观测器设计方法。最终的控制架构在基准系统上进行了测试，验证了其良好的控制性能和高效适用性。