"Control system performance is heavily dependent on the sensor signals used for feedback control; and therefore, sensor performance degradation and fault diagnostics are essential. Indeed, degradation of sensor performance or sensor fault due to sensor aging affect the closed-loop system performance, reliability, and even stability. On the other hand, the increasing need for safety and reliability has motivated the development of fault-tolerant control (FTC) techniques. This work proposes that sensor performance degradation and sensor sudden failure due to its aging can be characterized by the sensor measurement noise covariance variation (shift), while recent literature considers sensor fault and/or failure as an augmented state. That is, in this work gradual sensor performance degradation due to its aging can be characterized by gradual-variation of the time-varying sensor measurement noise covariance. In addition, sensor abrupt or intermittent fault or failure can be characterized by an intermittent or abrupt change of the time-varying sensor noise covariance. Furthermore, this work proposes fault detection algorithms to online monitoring sensor performance and online detecting and identifying sensor performance degradation and sudden (abrupt or intermittent) failure due to sensor aging. The proposed algorithms have two key features: online estimating the slowly-varying sensor measurement noise covariance and detecting the sudden (fast) change of the sensor measurement noise covariance. The first proposed algorithm shows the capability of estimating the slowly-varying sensor measurement noise covariance for multiple-input and multiple-output systems with time-varying sensor measurement noise covariance. Furthermore, the proposed estimation algorithm shows a reasonable rate of convergence, better estimation accuracy and less computation load in contrast to published literature. Moreover, the second proposed algorithm, which is a memory-based technique calculating the Euclidean distance of estimated covariance matrices between two sliding estimation windows is used to detect the abrupt (or intermittent) change of sensor noise covariance matrix. The proposed algorithm originally is designed for discrete linear time-varying (DLTV) systems and applied to discrete linear parameter-varying (DLPV) systems. The proposed algorithm shows the capability of detecting the abrupt (or intermittent) change of sensor measurement noise covariance for multiple-input and multiple-output discrete linear parameter-varying systems with time-varying sensor measurement noise covariance, where the scheduling parameters lie within a compact set. Furthermore, the proposed estimation algorithm shows a reasonable rate of convergence, better estimation accuracy and less computation load in contrast to published literature. The other major contribution of this work is the characterization of the control synthesis conditions using parametrized linear matrix inequalities (PLMI) for a multi-objective gain-scheduled noisy output-feedback controller that minimizes the output cost on H 2 performance with satisfactory system stability, H infinity performance, and control input covariance constraints ( H2 constraints on the control inputs) in the presence of sensor aging. The closed-loop system stability and performance, in terms of mixedH2/H infinity performances, relative improvement, numerical complexity, computation time and initial conditions response are studied. The synthesized controller guarantees not only the stability but also the closed-loop mixed H2/Hinfinity performances and it is feasible for real-time applications. To generate the output system performance, the output covariance constraints (OCC) control synthesis conditions are developed using parametrized linear matrix inequalities (PLMI) for a gain-scheduled noisy output-feedback controller that minimizes the cost on control input (control effort) with satisfactory system output covariance constraints in the presence of sensor aging. The closed-loop system performance in terms of control effort as a function of the output covariance and the sensor noise covariance is studied. The synthesized controller guarantees the closed-loop OCC performance and it is feasible for real-time applications. Therefore, the degradation of sensor performance or sensor fault due to sensor aging has been detected. The synthesized control utilizes sensor aging information to minimize its effect on the system and improves the closed-loop system performance as possible subject to given constraints and sensor performance degradation due to aging."--Pages ii-iii.
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