Fault detection and identification for a class of continuous piecewise affine systems with unknown subsystems and partitions
Author(s) -
Moustakis Nikolaos,
Zhou Bingyu,
Le Quang Thuan,
Baldi Simone
Publication year - 2018
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2881
Subject(s) - benchmark (surveying) , piecewise , affine transformation , parametric statistics , representation (politics) , identification (biology) , fault detection and isolation , class (philosophy) , computer science , control theory (sociology) , algorithm , mathematical optimization , mathematics , control (management) , artificial intelligence , law , mathematical analysis , statistics , botany , geodesy , politics , political science , pure mathematics , actuator , biology , geography
Summary This paper establishes a novel online fault detection and identification strategy for a class of continuous piecewise affine (PWA) systems, namely, bimodal and trimodal PWA systems. The main contributions with respect to the state‐of‐the‐art are the recursive nature of the proposed scheme and the consideration of parametric uncertainties in both partitions and in subsystems parameters. In order to handle this situation, we recast the continuous PWA into its max‐form representation and we exploit the recursive Newton‐Gauss algorithm on a suitable cost function to derive the adaptive laws to estimate online the unknown subsystem parameters, the partitions, and the loss in control authority for the PWA model. The effectiveness of the proposed methodology is verified via simulations applied to the benchmark example of a wheeled mobile robot.