Title: Nonlinear Data-Driven Control Part II: qLPV Predictive Control using Parameter Extrapolation Show Chinese title

Title: 非线性数据驱动控制 第二部分：使用参数外推的qLPV预测控制

Abstract: We present a novel data-driven Model Predictive Control (MPC) algorithm for nonlinear systems. The method is based on recent extensions of behavioural theory and Willem's Fundamental Lemma for nonlinear systems by the means of adequate Input-Output (IO) quasi-Linear Parameter Varying (qLPV) embeddings. Thus, the MPC is formulated to ensure regulation and IO constraints satisfaction, based only on measured datasets of sufficient length (and under persistent excitation). Instead of requiring the availability of the scheduling trajectories (as in recent papers), we consider an estimate of the function that maps the qLPV realisation. Specifically, we use an extrapolation procedure in order to generate the future scheduling trajectories, at each sample, which is shown to be convergent and generated bounded errors. Accordingly, we discuss the issues of closed-loop IO stability and recursive feasibility certificates of the method. The algorithm is tested and discussed with the aid of a numerical application. Show Chinese abstract

Abstract: 我们提出了一种新颖的数据驱动模型预测控制（MPC）算法，用于非线性系统。 该方法基于最近通过适当的输入输出（IO）准线性参数变化（qLPV）嵌入对非线性系统的行为理论和Willem基本引理的扩展。 因此，MPC被制定为仅基于足够长度的测量数据集（并在持续激励下）确保调节和IO约束的满足。 与近期论文中需要调度轨迹的可用性不同，我们考虑了一个对映射qLPV实现的函数的估计。 具体来说，我们在每个样本中使用一种外推程序来生成未来的调度轨迹，该程序被证明是收敛的并产生有界的误差。 相应地，我们讨论了闭环IO稳定性以及该方法的递归可行性证书。 该算法通过一个数值应用进行了测试和讨论。