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The computational simplicity of Fault Detection and Diagnosis (FDD) schemes based on parameter estimation, as well as their suitability for integrated design with Fault Tolerant Control (FTC), make them attractive from the point of view of their implementation within real-time practical applications. This paper focuses on the advantages that these methods can offer as opposed to state estimation and parity equations based system diagnosis, while also highlighting the disadvantages of the parameter estimation approach in view of identifying the problems that could benefit from a simultaneous state and parameter estimation based scheme. In order to achieve this objective, a system diagnosis procedure is proposed, which makes use of the Recursive Least Squares (RLS) method with exponential weighting and constant forgetting factor in view of detecting the occurrence of a fault within the system, as well as providing the post-fault system model. An application example from the aircraft control field is also presented in order to illustrate the efficiency of the proposed approach.

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This paper examines the fault-tolerant control problem for discrete-time descriptor systems that are susceptible to intermittent actuator failures, nonlinear sensor data, and probability-based missing data. The discrete-time non-homogeneous Markov chain was adopted to describe the stochastic behavior of actuator faults. Moreover, Bernoulli-distributed stochastic variables with known conditional probabilities were employed to describe the practical features of random sensor non-linearity and missing data. In this study, the output signals were quantized and a dynamic output feedback controller was synthesized such that the closed-loop system was stochastically admissible and satisfied the strictly $(\mathbf{Q},\mathbf{S},\mathbf{R})$-$\gamma$-dissipative performance index. The theoretical developments are illustrated through numerical simulations of an infinite machine bus.

Applied in many fields, nonlinear systems involving delay and algebraic equations are referred to as singular systems. These systems remain challenging due to saturation constraints that affect actuators and cause harm to their operation. Furthermore, the complexity of the problem will increase when uncertainty also simultaneously affects the system under consideration. To address this issue, this paper investigated a feasible control strategy for nonlinear singular systems with time-varying delay that are subject to uncertainty and actuator saturation. The IT-2 fuzzy model was adopted to describe the dynamic of the non-linear delayed systems using lower and upper membership functions to deal with the uncertainty. Moreover, the polyhedron model was applied to characterize the saturation function. The goal of the control approach was to design a relevant IT2 fuzzy state feedback controller with mismatched membership functions so that the closed-loop system is admissible. On the basis of an appropriate Lyapunov–Krasovskii functional, sufficient delay-dependent conditions were established and an optimization problem was formulated in terms of linear matrix inequality constraints to optimize the attraction domain. Simulation examples are provided to verify the effectiveness of the proposed method.