1,721,305 research outputs found
Discussion on "Modeling Identification and Compensation of Complex Hysteretic Nonlinearities. A Modified Prandtl-Ishlinskii Approach"
No full textA Stochastic Cellular Automaton for Modelling Radiation-Matter Interaction in Semiconduc tor Lasers
No full textThe paper presents a Cellular Automaton model of a
semiconductor laser. The basic physical laws of quantum
theory of light-matter interaction are directly described as
cellular evolution rules. The use of such a model allows a
deep insight in the fundamental properties of radiationmatter interaction in modern optical devices. Simulation results are presented in very good agreement with other
classical modelling techniques based on differential
equations and with experimental results
Cellular automata analysis of the structure of carrier lifetime and its multicausal nonlinear dependence in semiconductor lasers
No full textThe dependence of spontaneous carrier lifetime Ts suggests new experiments for the determination of microscopic parameters of the active region material. Cavity geometry's are time evolving structures due to the high non- linearities typical of the elementary processes involved. A computational approach basically different from the differential equation one, is being developed. The results obtained by this approach are compared with the analytical and the experimental ones, thus leading to the possible characterization of devices just at the wafer level. Finally complementary information are extracted from relaxation oscillations
Model-free actuator fault detection using a spectral estimation approach: the case of the DAMADICS benchmark problem
No full textIdentification of Nonlinear Models of Artificial Stimulation of the Quadriceps Muscle
No full textThe problem of identification of nonlinear models for the Functional Electrical Stimulation (FES) process is considered. In particular, the stimulation of the quadriceps muscle group and the following movement (or torque release) of the knee-joint will be considered. Both isometric and isotonic experimental conditions are considered. NARX models will be identified from data: polynomial and neural network structures are considered. For both model families, the structural identification problem and the model validation issue are considered. The resulting models are compared with experimental data measured on a paraplegic patient
Identification of Linear Parameter Varying Models using Kalman Filtering
No full textThe problem of estimating affine linear parametrically varying models is considered. An algorithm based on a Kalman filtering approach is proposed and its performance evaluated in a simulation study
Design of a gain scheduling controller for knee-joint angle control by using functional electrical stimulation
No full textA gain scheduling approach to the feedback control of the knee-joint movement in paraplegic patients, who have recovered partial functionality of muscles through functional electrical stimulation (FES), is studied. Since it was not possible to perform stimulation sessions on the patient during the work development, collected experimental data have been used to tune a known physiological model of the musculo-skeletal system involved FES, which is here adopted as a "virtual patient," i.e., as the system to control. So, a nonlinear black box model is developed, by using I/O data set obtained from the physiological model simulator. With reference to such a black box model a nonlinear gain scheduling controller is designed, by using the knee-joint position as a scheduling variable and by properly interpolating different linear quadratic regulators. It is proven that the linearization property holds for the proposed controller. Furthermore, the performance of such a controller are analyzed through closed-loop simulations, where the physiological model simulator is used to represent the knee-joint dynamics. The proposed controller shows good tracking and robustness properties on the full range of extension of the knee joint angle and simulation show that the presented strategy could perform better than any proposed linear approach to this problem
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
Identification of linear models for the dynamics of a photodetector
No full textIn this paper the dynamic characterisation of a novel optical sensor from experimental impulse response measurements is considered. In particular, subspace identification techniques are applied to the estimation of a state space model for the device. The obtained linear model is applied to the estimation of the linearity and the dynamic range of the sensor
Model-free fault detection: A spectral estimation approach based on coherency functions
No full textThis paper presents a model-free fault detection technique based on the use of a specific spectral analysis tool, namely, squared coherency functions. The fault-free dynamic behaviour of the plant considered is described by a stochastic linear state equation, where the stochastic part is due to unpredictable external disturbances. A fault is assumed to be a non-linear dynamic perturbation of the linear plant dynamics. The detection of the fault is achieved by on-line monitoring the estimates of a squared coherency function that is sensitive to the occurrences of non-linear events affecting the plant dynamics. A theoretical analysis of the fault-detectability issue is made and an original algorithm for a low-bias estimation of the squared coherency function is exploited to minimize the false-alarm rate. Finally, experimental results obtained by using real data concerning the three-tank benchmark problem are reported, showing the effectiveness of the proposed methodology
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