Stability and Performance-Based Strategies for Sensor Fault Accommodation in Multi-rate Sampled-data Processes

Abstract

The focus of this work is to give some insight into the effects of sensor faults on the stability and performance of sampled-data state feedback systems, and how these effects can be mitigated through use of fault-tolerant control. These methods are applied to a linearized model of a sampled-data system with uncertainty and access to full state measurements. The stability of the system is explicitly characterized as a function of the state sampling rates, fault parameters, fault accommodation measures, and state matrices. Sensor faults are discussed that manifest as improper sensor readings, and sensor sampling rate drift. The first fault updates the model of the system at sampling times to a factor of the actual measurement, the second type of fault alters the rate at which the sensors sample the system. In the presence of these faults the locations of the closed-loop poles are altered to achieve stability of the faulty system. Furthermore a chosen performance metric of the closed loop system is parameterized the same way as the stability of the system, for further insight into post fault operation. The explicit characterization of the stability and performance landscapes offer a look into the robustness and margins of tolerable faults that can be accommodated for and these benefits are discussed with some simulation results for context.