1,721,035 research outputs found
Model-free actuator fault detection using a spectral estimation approach: the case of the DAMADICS benchmark problem
No full textModel-free actuator fault detection using a spectral estimation approach:the case of the damadics benchmark problem
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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
A receding-horizon multiple model based control scheme for nonlinear systems
No full textA multiple model based nonlinear control scheme for nonlinear discrete-time systems is considered. A set of optimal receding-horizon feedback control functions is defined, based on a description of the nonlinear plant by means of interpolation of a set of local models, using a family of nonlinear validity functions. To obtain the control law to be applied to the plant, interpolation of the local control actions is done, using the same validity function family. This choice allows to carry out the stability analysis of the equilibrium of the multiple model control scheme
Identification 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
Gain scheduling control of Functional Electrical Stimulation for standing up and sitting down in paraplegia
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