A nonlinear model predictive control scheme for sensor fault tolerance in observation processes

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.

Adaptive neural tracking control for non‐affine switched systems with multiple time delays and actuator faults

“…To overcome these difficulties, it is very important to obtain all the information of the systems through limited information. Thus, various observers have been constructed for various systems, [21][22][23] which further promoted the development of control theory. In the work, 24 a robust switching controller was designed for servo control switched systems to reduce the switching gain.…”