This correspondence deals with the problem of robust fault detection for discrete-time switched systems with state delays under an arbitrary switching signal. The fault detection filter is used as the residual generator, in which the filter parameters are dependent on the system mode. Attention is focused on designing the robust fault detection filter such that, for unknown inputs, control inputs, and model uncertainties, the estimation error between the residuals and faults is minimized. The problem of robust fault detection is converted into an H infin-filtering problem. By a switched Lyapunov functional approach, a sufficient condition for the solvability of this problem is established in terms of linear matrix inequalities. A numerical example is provided to demonstrate the effectiveness of the proposed method

In this paper, we develop novel accuracy and performance guarantees for optimal state estimation of general nonlinear systems (in particular, moving horizon estimation, MHE). Our results rely on a turnpike property of the optimal state estimation problem, which essentially states that the omniscient infinite-horizon solution involving all past and future data serves as turnpike for the solutions of finite-horizon estimation problems involving a subset of the data. This leads to the surprising observation that MHE problems naturally exhibit a leaving arc, which may have a strong negative impact on the estimation accuracy. To address this, we propose a delayed MHE scheme, and we show that the resulting performance (both averaged and non-averaged) is approximately optimal and achieves bounded dynamic regret with respect to the infinite-horizon solution, with error terms that can be made arbitrarily small by an appropriate choice of the delay. In various simulation examples, we observe that already a very small delay in the MHE scheme is sufficient to significantly improve the overall estimation error by 20-25 % compared to standard MHE (without delay). This finding is of great importance for practical applications (especially for monitoring, fault detection, and parameter estimation) where a small delay in the estimation is rather irrelevant but may significantly improve the estimation results.

An approach is proposed in which fault detection and diagnosis (FDD) tasks are distributed to separate FDD modules associated with each control system located throughout a plant. Intended specifically for those control systems that inherently eliminate steady state error, it is modular, steady state based, requires very little process specific information and therefore should be attractive to control systems implementers who seek economies of scale. The approach is applicable to virtually all types of process plant, whether they are open loop stable or not, have a type or class number of zero or not and so on. Based on qualitative reasoning, the approach is founded on the application of control systems theory to single and cascade control systems with integral action. This results in the derivation of cause-effect knowledge and fault isolation procedures that take into account factors like interactions between control systems, and the availability of non-control-loop-based sensors

This paper presents an enhanced robust control design structure to realise fault tolerance towards sensor faults suitable for multi-input-multi-output (MIMO) systems implementation. The proposed design permits fault detection and controller elements to be designed with considerations to stability and robustness towards uncertainties besides multiple faults environment on a common mathematical platform. This framework can also cater to systems requiring fast responses. A design example is illustrated with a fast, multivariable and unstable system, that is, the double inverted pendulum system. Results indicate the potential of this design framework to handle fast systems with multiple sensor faults

We discuss the solution of the fault detection problem for parameter uncertain nonlinear systems using a gain scheduling based approach. By using a set of linearized plant models, a set of least order linear fault detection filters is determined to serve for interpolation based gain scheduling. The basic synthesis approach is the nullspace method for constant linear systems, whose extension to the case of linear parameter varying (LPV) models provides the methodological framework for the synthesis of robust fault detection filters. The resulting gain scheduled fault detection filter provides robustness with respect to both model nonlinearities as well as parametric uncertainties. In a general setting, we consider the case when part of the uncertain parameters are non-measurable and part of them are measurable

This paper is concerned with the development of a novel nonlinear observer-based scheme for early Fault Detection and Isolation (FDI) in wind turbines. The method is based on designing a nonlinear observer using State Dependent Differential Riccati Equation (SDDRE) and a nonlinear model of the 5MW wind turbine. The fault detection system is designed and optimized to be most sensitive to system faults and least sensitive to system disturbances and noises. The comparison of system outputs with nonlinear observer outputs are given to demonstrate good estimation performance. The residual generator based on the nonlinear observer is also employed to develop a monitoring system. Simulation results presented to illustrate that the proposed method is robust and can detect and isolate a fault or multi-faults in sensors of the wind turbine

We discuss the solution of the fault detection problem in presence of parametric uncertainties. The basic approach is an extension of the nullspace method for constant systems to the case of linear parameter varying (LPV) models. In a general setting, we consider the case when part of the unknown parameters are non-measurable and part of them are measurable. The resulting LPV-gain scheduled fault detection fi�lter provides robustness with respect to both types of parametric uncertainties. Symbolic and numerical computational procedures which underlie the proposed synthesis approach are discussed

We propose a numerically reliable computational approach to design fault detection filters for periodic systems. This approach is based on a new numerically stable algorithm to compute least order annihilators without explicitly building time-invariant lifted system representations. The main computation in this algorithm is the orthogonal reduction of a periodic matrix pair to a periodic Kronecker-liker form, from which the periodic realization of the detector is directly obtained

Link to a related website: http://vuir.vu.edu.au/7209/1/ACSP-09-0185.pdf, Open Access via UnpaywallThis paper focuses on the problem of active fault-tolerant control for switched systems with time delay. By utilizing the fault diagnosis observer, an adaptive fault estimate algorithm is proposed, which can estimate the fault signal fast and exactly. Meanwhile, a delay-dependent criterion is obtained with the purpose of reducing the conservatism of the adaptive observer design. Based on the fault estimation information, an observer-based fault-tolerant controller is designed to guarantee the stability of the closed-loop system. In terms of linear matrix inequality, sufficient conditions are derived for the existence of the adaptive observer and fault-tolerant controller. Finally, a numerical example is included to illustrate the efficiency of the proposed approach.Dongsheng Du, Bin Jiang, and Peng Sh

In this paper, a quantitative model based method is proposed for early fault detection and diagnosis of wind turbines. The method is based on designing an observer using a model of the system. The observer innovation signal is monitored to detect faults. For application to the wind turbines, a first principles nonlinear model with pitch angle and torque controllers is developed for simulation and then a simplified state space version of the model is derived for design. The fault detection system is designed and optimized to be most sensitive to system faults and least sensitive to system disturbances and noises. A multiobjective optimization method is then employed to solve this dual problem. Simulation results are presented to demonstrate the performance of the proposed method

The design problem of fault detection and isolation filters is formulated as a model matching problem and solved using an H2-or H1-norm optimization approach. A systematic procedure is proposed to choose appropriate filter specifications which guarantee the existence of proper and stable solutions of the model matching problem. This selection is integral part of numerically reliable computational methods to design of H2 -or H1-optimal fault detection filters. The proposed design approach is completely general, being applicable to both continuous-and discrete-time systems, and can easily handle even unstable and/or improper systems