This paper proposes a novel clustering-based approach to the bounded-error identification of switched and piecewise affine autoregressive exogenous systems. We address the problem of determining a minimal collection of linear-in-the-parameters models (called modes) fitting with a given accuracy $\varepsilon$ a set of input–output data while complying with the switched or piecewise affine nature of the system. The problem is tackled by suitably clustering the data according to their preferences with respect to a pool of candidate models identified on subsets of the available data. The preference of a data point for a model is assessed based on the extent to which that model fits that data point and is set to zero if the fit is worse than $\varepsilon$. A two-level clustering with outliers isolation is employed, first grouping data based on their preferences subject to suitable time/space adjacency conditions depending on the nature of the switching mechanism, and then collecting together non-adjacent clusters that can be described by the same mode. The performance of the proposed method is demonstrated via comparative numerical examples and on experimental data from an electronic component placement process in a pick-and-place machine.

本文提出了一种新的基于聚类的方法来识别切换和分段仿射自回归外生系统的有界误差。我们解决了确定以给定精度拟合的线性参数模型（称为模式）的最小集合的问题$\epsilon$一组输入-输出数据，同时符合系统的切换或分段仿射性质。这个问题是通过根据他们的偏好对可用数据子集上确定的候选模型池的数据进行适当聚类来解决的。模型的数据点的偏好是根据该模型拟合该数据点的程度来评估的，如果拟合差则设置为零$\epsilon$. 采用具有异常值隔离的两级聚类，首先根据数据的偏好对数据进行分组，根据切换机制的性质在合适的时间/空间邻接条件下，然后将可以由相同描述的非相邻簇收集在一起模式。通过比较数值示例和取放机中电子元件放置过程的实验数据证明了所提出方法的性能。