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A robust virtual sensor design is the aim of this paper. Such a sensor is used in a fault-tolerant control scheme which can be applied to wide class of non-linear systems. To deal with system non-linearity, a linear parameter-varying systems are considered. A robust virtual sensor is developed in such a way that the level of disturbances attenuation can be reached in relation to the fault estimation...
An Unknown Input Observer design for the sensors fault diagnosis of a non-linear system is the aim of this paper. To detect the faulty sensor, the method based on output residual is used. To obtain such a residual signal, an observer-based approach is proposed. How to design such observer is explained in details. Moreover, the two-tank system fault-tolerant control method is proposed. An experimental...
The main objective of this paper is to develop and design a State Space Neural Network toolbox for a non-linear system modeling with an artificial state-space neural networks, which can be used in a model-based robust fault diagnosis and control. Such a toolbox is implemented in the MATLAB environment and it uses some of its predefined functions. It is designed in the way that any non-linear multi-input...
This paper presents an design of a Robust Fault Detection and Isolation (FDI) diagnostic system by the means of state-space neural network. First, an solution utilizing multimodel technique is described, in which a Single-Input MultiOutput (SIMO) system is decomposed into a number of Multi-Input Single-Output (MISO) and Single-Input Single-Output (SISO) models. Application of such models makes possible...
The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H∞...
In this paper, a discrete-time Linear Parameter-Varying (LPV) system identification method using artificial neural network is described. In particular, neural network is transformed to obtain LPV model of the non-linear system. Moreover, a novel robust fault diagnosis scheme is developed, which is based on an observer within H∞ framework for a class of non-linear systems. The effectiveness of the...
The paper deals with the problem of robust fault-tolerant model predictive control for non-linear discrete-time systems described by the Linear Parameter-Varying model. The proposed approach is based on a multi-stage stage procedure. Robust controller is designed without taking into account the input constraints related with the actuator saturation and deals with previously estimated faults. Thus,...
The paper is concerned with the task of robust fault estimation of non-linear discrete-time systems. The general unknown input observer strategy and the ℋ∞ framework are utilised to design a robust fault estimation scheme. The resulting design procedure guaranties that a prescribed disturbance attenuation level is achieved with respect to the fault estimation error while guaranteeing the convergence...
The main contribution of the paper was to propose a robust predictive fault-tolerant control scheme for a class of non-linear discrete-time systems that can be described with the LPV models using neural networks. Indeed, the contribution can be divided into a few important points: extension of the efficient predictive control to the robust case with exogenous external disturbances acting on the system,...
The paper deals with the problem of robust faulttolerant control (FTC) for non-linear systems. Main part of this paper describes a robust fault detection, isolation and identification scheme, which is based on the observer and ℌ∞ framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the...
The paper deals with the problem of a robust actuator fault diagnosis for Linear Parameter-Varying (LPV) systems with Recurrent Neural-Network (RNN). The preliminary part of the paper describes the derivation of a discrete-time polytopic LPV model with RNN. Subsequently, a robust fault detection, isolation and identification scheme is developed, which is based on the observer and H∞ framework for...
In this paper, the actuators and sensors fault detection and localization using a system model is considered. To obtain the system model, the neural network modeling is used. The artificial feedforward neural network with dynamic neurons in the state-space representation is proposed. To estimate the neural network parameters, the Adaptive Random Search algorithm with projection is used. To identify,...
This paper presents an identification method of dynamic systems based on the Group Method of Data Handing. In particular, a new structure of the dynamic neuron in pole representation is proposed. Moreover, a new training algorithm based on the Unscented Kalman Filter is presented. The final part of this work contains an illustrative example regarding the application of the proposed approach to an...
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