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hybrid robust fault detection and isolation of satellite reaction wheel actuators

Journal of Control and Decision 2024年第1期11卷 117-131页

作者： h.abbasi nozari S.J.Sadati Rostami Paolo Castaldi Silvio Simani Faculty of Electrical and Computer Engineering Babol Noshirvani University of TechnologyBabolIran Department of Electrical Electronic and Information EngineeringUniversity of BolognaCesenaItaly Department of Engineering University of FerraraFerraraItaly

In this paper,a combined robust fault detection and isolation scheme is studied for satellite system subject to actuator faults,external disturbances,and parametric *** proposed methodology incorporates a residual generation module,including a bank of filters,into an intelligent residual evaluation ***,residual filters are designed based on an improved nonlinear differential algebraic approach so that they are not affected by external *** residual evaluation module is developed based on the suggested series and parallel ***,a new ensemble classification scheme defined as blended learning integrates heterogeneous classifiers to enhance the performance.A wide range of simulations is carried out in a high-fidelity satellite simulator subject to the constant and time-varying actuator faults in the presence of disturbances,manoeuvres,uncertainties,and *** obtained results demonstrate the effectiveness of the proposed robust fault detection and isolation method compared to the traditional nonlinear differential algebraic approach.

关键词： Robust fault detection and isolation reaction wheels blended learning series and parallel fault detection and isolation forms satellite attitude control system

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Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks

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Journal of Zhejiang University-Science C(Computers and Electronics) 2012年第6期13卷 403-412页

作者： hasan abbasi nozari hamed DEhGhAN BANADAKI Mohammad MOKhTARE Somayeh hEKMATI VAhED Department of Mechatronics Faculty of EngineeringIslamic Azad UniversityScience and Research BranchTehranIran

This study deals with the neuro-fuzzy (NF) modelling of a real industrial winding process in which the acquired NF model can be exploited to improve control performance and achieve a robust fault-tolerant system. A new simulator model is proposed for a winding process using non-linear identification based on a recurrent local linear neuro-fuzzy (RLLNF) network trained by local linear model tree (LOLIMOT), which is an incremental tree-based learning algorithm. The proposed NF models are compared with other known intelligent identifiers, namely multilayer perceptron (MLP) and radial basis function (RBF). Comparison of our proposed non-linear models and associated models obtained through the least square error (LSE) technique (the optimal modelling method for linear systems) confirms that the winding process is a non-linear system. Experimental results show the effectiveness of our proposed NF modelling approach.

关键词： Non-linear system identification Recurrent local linear neuro-fuzzy (RLLNF) network Local linear model tree(LOLIMOT) Neural network (NN) Industrial winding process

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Use of Artificial Neural Network process analyzers: A case study

Use of Artificial Neural Network process analyzers: A case s...

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2012 IEEE International Conference on Computer Science and Automation Engineering（CSAE 2012）

作者： Mahdi khosravi Amin Khosravi Mehran Khosravi Parastoo nozari Instrumentation Department forth South Pars Gas Refining Co. Iranian National gas Company Asalooyeh Iran

In this paper artificial neural network （ANN）, which are known for ability to model non linear system, are used as h s analyzer to predict h s in export gas at South Pars Gas company in south of Iran. The training data has been collected over duration of three years and use to train a neural network to develop based h s analyzer. The results are promising. The FPD become the most commonly used sulfur detector due to its ease-of-use and relatively low cost of operation. It also exhibits moderate sensitivity and selectivity for sulfur. however, the FPD has a number of disadvantages, a key disadvantages is that its response to sulfur is approximately proportional to the square of the mass of the sulfur compound. Nowadays PFDD used as sulfur detector, it is a significant improvement because it can provide better sensitivity and selectivity for sulfur and *** use of PFDD increasing both costs and complexity. The adopted neural network used is a multi layer feed forward, network with Inputs, one output trained using back propagation algorithm. After many trails, it has been found neurons in the hidden layer give an acceptable compromise between network estimation accuracy and network complexity.

关键词： Artificial neural network Gas chromatography hydrogen sulfide

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Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks

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Frontiers of Information Technology & Electronic Engineering 2012年第9期 701-701页

作者： hasan abbasi nozari hamed Dehghan Banadaki Mohammad Mokhtare Somayeh hekmati Vahed

关键词： NF Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks

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