| Summary: | Owing to the need for the satisfaction of attributes such as safety, maintainability, and reliability in modern critical engineering devices, the design of automatic feedback control systems has increasingly demanding fault-tolerant methods. In particular, if the system states cannot directly be measured by the available suite of sensors, a fault-tolerant state estimation method turns out to be of paramount importance for achieving fault tolerance. In this context, the present thesis formulates a fault-tolerant state estimation (FTSE) problem consisting of a joint state and fault estimation of linear systems subject to additive faults. The system is described by a discrete-time linear Gaussian state-space model, where the fault appears as unknown inputs affecting both the state and measurement equations. The sequence of fault inputs is assumed to be parameterizable by three fault parameters: the fault magnitude, the fault instant, and the fault mode index. Moreover, these parameters are treated as unknown realizations of random variables (RV) that are defined so as to account for prior knowledge about possible faults. For tackling the above FTSE problem, the present work introduces a fault-tolerant two-stage (FTTS) filtering approach, from which three different FTTS filters are derived by considering three plausible alternative characterizations of the fault magnitude RV. On the basis of computational simulations, one of the FTTS filters is illustrated on a fault-tolerant model predictive control (MPC) scheme for satellite attitude control.
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