In this paper, we propose a novel Nonlinear Model Predictive Control (NMPC) framework for tracking for piece-wise constant reference signals. The main novelty is the use of quasi-Linear Parameter Varying (qLPV) embeddings in order to describe the nonlinear dynamics. Furthermore, these embeddings are exploited by an extrapolation mechanism, which provides the future behaviour of the scheduling parameters with bounded estimation error. Therefore, the resulting NMPC becomes computationally efficient (comparable to a Quadratic Programming algorithm), since, at each sampling period, the predictions are linear. Benefiting from artificial target variables, the method is also able to avoid feasibility losses due to large set-point variations. Robust constraint satisfaction, closed-loop stability, and recursive feasibility certificates are provided, thanks to uncertainty propagation zonotopes and parameter-dependent terminal ingredients. A benchmark example is used to illustrate the effectiveness of the method, which is compared to state-of-the-art techniques.

中文翻译：

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使用 qLPV 嵌入和区域不确定性传播的鲁棒非线性跟踪 MPC

在本文中，我们提出了一种新颖的非线性模型预测控制（NMPC）框架，用于跟踪分段恒定参考信号。主要新颖之处是使用准线性参数变化（qLPV）嵌入来描述非线性动力学。此外，这些嵌入被外推机制利用，该机制提供了具有有限估计误差的调度参数的未来行为。因此，生成的 NMPC 的计算效率很高（与二次规划算法相比），因为在每个采样周期，预测都是线性的。受益于人工目标变量，该方法还能够避免由于设定点变化较大而造成的可行性损失。由于不确定性传播区域和参数相关的终端成分，提供了鲁棒的约束满足、闭环稳定性和递归可行性证书。使用基准示例来说明该方法的有效性，并将其与最先进的技术进行比较。