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The paper studies the fault identification problem for nonlinear control systems under the unmatched disturbances. A novel approach to construct sliding mode observer is proposed for systems that do not satisfy common conditions required for fault estimation, in particular, minimum phase, matching, and detectability conditions. The suggested approach is based on the reduced order model of the original system invariant with respect to the disturbances. This allows to reduce complexity of sliding mode observer and relax the limitations imposed on the original system.

The problem of functional diagnosis of technical systems described by linear dynamic models under disturbances is considered. For solving the problems of detection, isolation and identification of faults, a method based on sliding mode observers is used. Modifications of this method for relaxing the implementation constraints in comparison with the well-known results and also for reducing the complexity of diagnostic tools are proposed.

The problem of robust fault diagnosis in nonlinear dynamic systems under presence of disturbances is studied within the scope of analytical redundancy conception. Solution of the problem assumes residual generation by checking redundancy relations existing among system inputs and outputs measured over a finite time window followed by decision making through evaluation of the residuals. Nonparametric method is considered for residual generation. To make decision, the unified method is developed whose feature is it combines the threshold logic of decision making and the residuals comparing with the fault syndromes evaluated on-line. Joint application of both nonparametric method for residual generation and unified method for decision making gives the possibility to develop the universal diagnostic platform for different systems described by ordinary nonlinear differential equations of the same structure but distinguished by these equations coefficients values.

The problem of fault diagnosis in technical systems described by linear dynamic models is considered. The approach allowing increase robustness of the diagnostic process under disturbances is sugge...

The problem of fault diagnosis in hybrid systems is investigated. The hybrid systems under consideration consist of finite automaton, the set of nonlinear difference equations and so-called mode activator that coordinates the action of these two parts. The hybrid residual generator based on the nonparametric method is found that solves the fault diagnosis problem under certain (sufficient) solvability conditions. Examples illustrate details of the solution.

The problem of fault diagnosis in technical systems described by linear dynamic models is considered. The approach allowing increase robustness of the diagnostic process under disturbances is suggested. The approach includes two stages: 1) synthesis of sliding mode observer, sensitive to the disturbance and invariant with respect to the fault; this observer estimates the value of disturbance, 2) synthesis of diagnostic observer, sensitive to the disturbance and fault, using the estimated value of disturbance; this observer provides fault detection. In the multi-fault complex of diagnostic tools is constructed.

This article is basically focused on application of the Robust Kalman Filter (RKF) algorithm to the estimation of high speed an autonomous underwater vehicle (AUV) dynamics In the normal operation conditions of AUV, conventional Kalman filter gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunction in the estimation system, Kalman Filter (KF) gives inaccurate results and diverges by time. This study, introduces Robust Kalman Filter algorithm with the filter gain correction for the case of measurement malfunctions. By the use of defined variables named as measurement noise scale factor, the faulty measurements are taken into the consideration with a small weight and the estimations are corrected without affecting the characteristic of the accurate ones. In the presented RUKF, the filter gain correction is performed only in the case of malfunctions in the measurement system and in all other cases procedure is run optimally with regular KF. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

The paper is devoted to the problem of fault isolation in technical systems described with linear dynamic models. So-called non-parametric, or model-free, or data-driven method is used to solve the problem. The feature of this method is that parameters of the system under consideration may be unknown. The methods of fault isolation and residual generation are suggested. The theoretical results are illustrated by an example.

The problem of fault accommodation in discrete-event systems is considered. Solution of the problem is related to constructing the control law which provides full decoupling with respect to fault effects. Existing conditions are formulated and calculating relations are given for the control law determination.