Abstract
SummaryThis 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.
Highlights
The problem of controlled sensing consists of driving an observation process to improve the quality of a desired estimate of some system
In order to address the control problem presented in the previous section, we use an observability-based nonlinear model predictive control (NMPC) approach where the system is steered to maintain a specific set of states and parameters observable when using a varying observation model
The proposed scheme lends itself to adaptive fault-tolerant NMPC (FTNMPC) and does not, compared with the majority of FTNMPC configurations in the literature, rely on switched controller reconfiguration set externally by a fault diagnosis unit
Summary
The problem of controlled sensing consists of driving an observation process to improve the quality of a desired estimate of some system. We consider the particular problem of estimating the state of an exogenous system in the case where the observation process is subject to potential sensor faults, upon which the system seeks to detect and isolate the faults and use the remaining sensing capabilities to maintain a high-quality estimate of the state of the exogenous system. In order to address the control problem presented, we use an observability-based NMPC approach where the system is steered to maintain a specific set of states and parameters observable when using a varying observation model. Toward this goal, this section recalls some results from the literature on observability and NMPC. Let the r-length observation map of (7) be defined as
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