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，探讨了非线性模型预测控制（NMPC）策略的设计。特别地，我们提出了一种新颖的公式化方法，该方法无需计算繁琐的终端约束，而是通过明确定义保证闭环稳定性的最小预测时域。该设计方法尤其适用于由循环神经网络（RNN）学习的系统控制——RNN以其强大的建模能力著称，且可通过简单的代数条件分析其增量ISS特性。我们将该方法应用于门控循环单元（GRU）网络，同时设计了一种具有收敛保证的专门状态观测器。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与高效适用性。