Recent Techniques for the Identification of Piecewise Affine and Hybrid Systems

Abstract

Summary. The problem of piecewise affine identification is addressed by studying four recently proposed techniques for the identification of PWARX/HHARX models, namely a Bayesian procedure, a bounded-error procedure, a clustering-based procedure and a mixed-integer programming procedure. The four techniques are compared on suitably defined one-dimensional examples, which help to highlight the features of the different approaches with respect to classification, noise and tuning parameters. The procedures are also tested on the experimental identification of the electronic component placement process in pick-and-place machines.

Identification of switched linear regression models using sum-of-norms regularization

“…Figure 4 shows the simulated output using the identified PWARX model and the measured output. This is slightly better than the result reported in [18] for a PWARX of the form (21) with two submodels. Note that this is a considerably more difficult task than the one-step ahead prediction.…”

“…The data used is of a real physical process and also used in [15,17,3,18]. It consists of a 15 s recording of the voltage input to the motor of the mounting head of the pickand-place machine (will be referred to as input) and the vertical position of the mounting head (will be referred to as output).…”

Identification of Hybrid Systems A Tutorial

“…The four identification procedures described in this section are compared and discussed in [44] (see also [45]). Specific behaviors of the procedures with respect to classification accuracy, noise level, and tuning parameters are pointed out using simple one-dimensional examples.…”

PWARX Model Identification Based on Clustering Approach

“…To achieve the purpose of these simulations, we consider the following quality measures (Juloski et al 2006):…”