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A nonlinear model predictive control scheme for sensor fault tolerance in observation processes
Author(s) -
Knudsen Brage R.,
Alessandretti Andrea,
Jones Colin N.,
Foss Bjarne
Publication year - 2020
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5104
Subject(s) - observability , control theory (sociology) , control reconfiguration , model predictive control , parametrization (atmospheric modeling) , nonlinear system , controller (irrigation) , fault detection and isolation , control engineering , scheme (mathematics) , computer science , fault (geology) , process (computing) , engineering , control (management) , mathematics , artificial intelligence , actuator , mathematical analysis , physics , quantum mechanics , seismology , geology , agronomy , biology , embedded system , radiative transfer , operating system
Summary This article addresses the problem of designing a sensor fault‐tolerant controller for an observation process where a primary, controlled system observes, through a set of measurements, an exogenous system to estimate the state of this system. We consider sensor faults captured by a change in a set of sensor parameters affecting the measurements. Using this parametrization, we present a nonlinear model predictive control (NMPC) scheme to control the observation process and actively detect and estimate possible sensor faults, with adaptive controller reconfiguration to optimize the use of the remaining sensing capabilities. A key feature of the proposed scheme is the design of observability indices for the NMPC stage cost to improve the observability of both the state of the exogenous system and the sensor fault parameters. The effectiveness of the proposed scheme is illustrated through numerical simulations.