一种无需稳定性相关终端约束的基于收缩的NMPC新公式
A New Contraction-Based NMPC Formulation Without Stability-Related terminal Constraints
AI总结：
本文提出一种无需显式包含收缩约束的基于收缩的NMPC公式，证明在温和假设下闭环行为的收敛性。
摘要：
基于收缩的非线性模型预测控制（NMPC）公式因其通常较短的预测 horizon 和无需终端集计算而具有吸引力，这些通常是保证稳定性的必要条件。然而，在底层优化问题定义中包含收缩约束往往导致非标准特征，如需要多步开环应用控制序列或使用多步记忆收缩水平，这可能在存在意外扰动时导致不可行。本文提出了一种新的基于收缩的NMPC公式，其中不显式包含收缩约束。在温和假设下，所得到的闭环行为的收敛性得到了证明。
英文摘要：
Contraction-Based Nonlinear Model Predictive Control (NMPC) formulations are attractive because of the generally short prediction horizons they require and the needless use of terminal set computation that are commonly necessary to guarantee stability. However, the inclusion of the contraction constraint in the definition of the underlying optimization problem often leads to non standard features such as the need for multi-step open-loop application of control sequences or the use of multi-step memorization of the contraction level that may induce unfeasibility in presence of unexpected disturbance. This paper proposes a new formulation of contraction-based NMPC in which no contraction constraint is explicitly involved. Convergence of the resulting closed-loop behavior is proved under mild assumptions.
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