268 research outputs found
Model-free actuator fault detection using a spectral estimation approach: the case of the DAMADICS benchmark problem
No full text
Identification of a class of nonlinear parametrically-varying models
No full textA novel class of linear time-varying models is proposed for nonlinear system identification purposes. These models are linear in the parameters, which are time-varying according to a nonlinear dynamic law. A specific parameter tuning algorithm is presented, which is based only on input/output measurements, but which also provides an estimate of the timevarying behaviour of the parameters. So, a Linear Parameter Varying (LPV) model is obtained to which is possible to apply the robust control techniques for LPV systems. Finally,
some interesting relations between this model class and the Local Model Network (LMN) family are discussed in the framework of the LPV systems obtained by convex combination of Linear-Time-Invariant (LTI) systems
Model-free actuator fault detection using a spectral estimation approach:the case of the damadics benchmark problem
No full textIdentification of a class of nonlinear, parametrically varying models
No full textThe aim of this paper is to propose a novel class of non-linear, possibly parameter-varying models suitable for system identification purposes. These models are given in the form of a linear fractional transformation (LFT) where the 'forward' part is represented by a conventional linear regression and the 'feedback' part is given by a non-linear dynamic map parameterized by a neural network (NN) which can take into account scheduling variables available for measurement. For this specific model structure a parameter estimation procedure has been set up, which turns out to be particularly efficient from the computational point of view. Also, it is possible to establish a connection between this model class and the well known class of local model networks (LMNs): this aspect is investigated in the paper. Finally, we have applied the proposed identification procedure to the problem of determining accurate non-linear models for knee joint dynamics in paraplegic patients, within the framework of a functional electrical stimulation (FES) rehabilitation engineering project
Identification of nonlinear parametrically varying models using separable least squares
No full textThe aim of this paper is to propose a novel identification aalgorithm based on separable least squares ideas, for a class of nonlinear, possibly parameter-varying, input/output
models. These models are given in the form of a Linear Fractional Transformation (LFT)
where the "forward" part is represented by a conventional linear regression and the "feedback"
part is given by a nonlinear map which can take into account scheduling variables available
for measurement. The nonlinear part of the model can be parameterised according to various
paradigms, like, e.g., Neural Network (NN) or NARX
Closed Loop Control of FES Supported Standing Up and Sitting Down Using Virtual Reference Feedback Tuning: a simulation study
No full textElectro-Mechanical Actuators for the More Electric Aircraft (Fault diagnosis and condition monitoring approaches)
No full textThis chapter presents the basic concepts of condition monitoring and fault diagnosis, with special attention to the definition of terminology and design approaches. Actually, the terminology in the field is not consistent, since it often depends on research context, application, and publication period. The concepts of fault detection, fault isolation, fault identification, fault estimation are systematically presented and clarified, with precise definitions of fault, malfunction, failure, residual, disturbance, and error. The specificities of condition monitoring and fault diagnosis are highlighted, and the various approaches to implement them are discussed
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