Model predictive controllers (MPC) with the two-loop scheme are successful approaches practically and can be classified into two main categories, tube-based MPC and MPC-based reference governors (RG). In this paper, an enhanced two-loop MPC design is proposed for a pre-stabilized system with the bounded uncertainty subject to the input and state constraints. The proposed method offers less conservatism than the tube-based MPC methods by enlarging the restricted input constraint. Contrary to the MPC-based RGs, the investigated method improves tracking performance of the pre-stabilized system while satisfying the constraints. Additionally, the robust global asymptotic stability of the closed-loop system is guaranteed in a novel procedure with terminal constraint relaxation. Simulation of the proposed method on a servo system shows its effectiveness in comparison to the others.

中文翻译：

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线性不确定系统的增强型两环模型预测控制设计

具有两环方案的模型预测控制器（MPC）实际上是成功的方法，可以分为两大类，即基于管的MPC和基于MPC的参考调速器（RG）。在本文中，针对具有输入和状态约束的有界不确定性的预稳定系统，提出了一种增强的两环MPC设计。与基于管的MPC方法相比，所提出的方法通过扩大受限的输入约束提供了较少的保守性。与基于MPC的RGs相反，研究的方法在满足约束条件的情况下提高了预稳定系统的跟踪性能。此外，在具有终端约束放松的新颖过程中，可以确保闭环系统的鲁棒全局渐近稳定性。