Dynamic System Identification: Experiment Design and Data Analysis


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This toolbox is commercially supported by The Mathworks. A collection of MATLAB routines developed and used by NASA, implementing a wide variety of experiment design, data analysis, and modeling methods, with emphasis on aircraft system identification. A freely available system identification toolbox for use with Matlab and Octave. Skip to Main Content Area.

Mathworks Matlab System Identification Toolbox An extensive suite of software tools for system identification for use with Matlab. Abstract: In the field of system identification and controla mismatch exists between the available theoretical tools andmost of the problems encountered in practice. On the one hand,researchers developed plenty of theoretical analysis and On the one hand,researchers developed plenty of theoretical analysis and methodsconcerning linear systems; on the other hand practitioners areoften confronted with the apparent nonlinearity of the real world.

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Although several nonlinear identification and control techniqueshave been proposed in the last decades, these still appear to be Data-driven calibration of power conductors thermal model for overhead lines overload protection. The increasing complexity of transmission networks may cause a significant rise in the load flows during and after serious system disturbances.

In this context, accurate thermal rating assessment of overhead lines is essential to maximise In this context, accurate thermal rating assessment of overhead lines is essential to maximise infrastructure utilisation, while ensuring a reliable functioning of the power networks. Thermal assessment demands reliable simulation models to provide short and long term predictions of thermal behaviour.

Although many physical based models are available nowadays, the incomplete From linearization to lazy learning: a survey of divide-and-conquer techniques for nonlinear control. Abstract—In the field of system identification and control a mismatch exists between the available theoretical tools and most of the problems encountered in practice.

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On the one hand, researchers developed plenty of theoretical analysis On the one hand, researchers developed plenty of theoretical analysis and methods concerning linear systems; on the other hand practitioners are often confronted with the apparent nonlinearity of the real world. Although several nonlinear identification and control techniques have been proposed in the last decades, these still appear to be less robust and reliable than their The Problem of Identification.

This paper presents the methodology of design of a discrete-time adaptive quasi-sliding mode controller QSMC based on a recursive weighted least square RWLS estimator for a dc motor.

The proposed control scheme allows handling the The proposed control scheme allows handling the classic problem of a QSMCs, which is the steady-state error due to the use of a saturation function instead of a switching function in the sliding mode control SMC algorithm. The use of linear and nonlinear signal references helps to show the closed-loop performance of the control system and its tracking capabilities. Juan P. One of the key issues in modelling for condition monitoring is how to accommodate the level of detail of the model description in order to suit the diagnostic requirements.

The paper addresses a two-stage modelling concept, which tends The paper addresses a two-stage modelling concept, which tends to result in highly accurate grey models by combining prior knowledge and recorded data. A class of continuous-time models linear in parameters is treated. The concept is used to design a diagnostic system for the heat transfer process in a tyre production plant.

An excerpt of the results obtained in operating records is given. Keywords: Condition monitoring, model-based diagnosis, modelling, identification, instrumental variables. The use of experimental data in simulation model validation.

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The use of experimental data for the validation of deterministic dynamic simulation models based on sets of ordinary differential equations and algebraic equations is discussed. Comparisons of model and target system data are considered Comparisons of model and target system data are considered using graphical methods and quantitative measures in the time and frequency domains.

System identification and parameter estimation methods are emphasized, especially in terms of identifiability analysis which can provide valuable information for experiment design. In general, experiments that are suitable for system identification are also appropriate for model validation. However, there is a dilemma since models are needed for this design process. The experiment design, data collection and analysis of model validation results is, inevitably, an iterative process and experiments designed for model validation can never be truly optimal.

A model of the pulmonary gas exchange processes in humans is used to illustrate some issues of identifiability, experiment design and test input selection for model validation. In this paper, we present a measurement model for estimating the magnetic field of a synchrotron-type particle accelerator, based on sensors installed in a reference magnet.

The model combines the calibration of the individual sensors The model combines the calibration of the individual sensors with the experimental characterization of the magnets to infer, in absolute terms, the value of the average field in the ring, as needed for the real-time feedback control of the accelerator.

We describe first the measurement setup and method, followed by the detailed definition of the model, along with its parameters and an evaluation of their value and uncertainty. Next, we assess the combined uncertainty of the whole measurement chain. Finally, we discuss the results obtained so far during the machine commissioning phase and outline our plans for future improvement.

Index Terms-Induction coil, measurement model, nuclear magnetic resonance NMR probe, real-time magnetic field measurement , synchrotron bending dipole. Identifying spectrum usage by unknown systems using experiments in machine learning. ABSTRACT We adopt a machine learning approach towards the problem of identifying wireless systems present in a dynamic radio environment with heterogeneous usage.

Two issues arise that necessitate validation studies. For completeness, these issues are briefly discussed. First, the parameters identified are those that best fit the data set using a specified model structure. To test the assumptions and prior knowledge that were used to achieve the desired model structure, validation against additional datasets is essential.

System Identification: Regression Models

Second, conclusions drawn from analysis of a dataset, whether qualitative such as interactions or quantitative such as parameter estimates are only as good as the dataset that was used to arrive at the knowledge. Improper experimental design can lead to invalid conclusions that can only be found by careful validation of the model against additional well designed experiments. A common practice in experimental biology has been to vary one input to the biological system at a time and see how the system responds; e.

Often, these changes are introduced as a step change in environmental conditions. Figure 5 A panels A and B demonstrates the effectiveness of step response data in generating linear models.


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Three linear models were identified from step response data Figure 5 A. The first data set consisted of three experiments. In each experiment, only one of the three system inputs was changed. This explored the effect of simultaneously varying all three inputs. The third data set explored the effect of sequentially varying each of the inputs. The model performance for each of the models identified by each of the three step change datasets described above was assessed. The results demonstrate that step changes were ineffective at capturing the inherent dynamics of the underlying system Figure 5 A.

For each of the three step change models identified, substantial errors were observed compared with the linear Taylor series model. Comparison of the full nonlinear model to the linear model identified using a Taylor series expansion.

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The model performance based on data from a series of random inputs to each of the three inputs was compared with the model performance derived from the step changes Figure 5 A. This corresponded to a total of 15 data points for each concentration species, the same as in the first step inputs experimental case. The performance of the linear Taylor series model was more closely approximated by the random inputs model than by the step change model.

In particular, the random inputs model demonstrated less error and a tighter distribution around the true predicted values than the models identified from step change data. Model performance for the identified models. In this study, it was assumed that the model structure was known i. In such a case, where the degree of interaction is not fully characterized and the inherent dynamic properties of the system are not available, random input sequences such as those described above will perturb the system in most directions i.


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  8. These random sequences will thus provide estimates of the key parameters within the network. Further, performing a series of step experiments to identify the model parameters will not provide reliable estimates [ 38 , 39 ]. Therefore, a random input profile will provide the greatest insight. To address this concern, the effect of increasing the sampling frequency for the step case on the quality of the predictions was determined Figure 5 B.

    Using the sampling frequency twice once every 7. This indicated that the improved identification was due to dynamic excitation and not to measurement frequency. However, with the random inputs, sampling frequency had a profound effect on the model identification Figure 5 C. Increasing the sampling frequency from once every 7. At higher frequencies, noise in the dataset masks true dynamic features.

    As a consequence, erroneous parameter estimates are obtained that capture the dynamics of the noise. This demonstrated that there exists an optimal sampling frequency based on the perturbation frequency, the inherent system dynamics, and the noise in the dataset. Efficient and effective experimental design requires a priori identification of these optimal frequencies.

    For the step responses, no significant effect was observed as the noise level changed. The identified model was equally poor in each case. As the noise level decreased in the random input datasets, the identified model approached the linear Taylor series approximation. Thus, random perturbations were able to better extract underlying dynamic features from noisy data. Again, the number of data points was conserved across comparisons: each model was identified with a total of 15 data points. Effect of noise on identified model quality. Data sets contained random Gaussian noise of the indicated level.

    A Performance of models identified from a series of three experiments, each consisting of a step change in one input. B Performance of models identified from random, independent input perturbations.


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    The effect of the perturbation frequency on model quality was investigated. In addition to the optimal sampling frequency identified above, an optimal perturbation frequency exists Figure 7.

    System identification

    Analysis of the linear Taylor series model provided insight into why this was the case. Increasing the perturbation and measurement frequencies masked the dynamics of the slowest mode by limiting it to a much smaller fraction of change. Lower perturbation and measurement frequencies missed dynamic information by allowing the fastest mode to approach steady-state. Thus, a tradeoff between the competing modes existed.

    For complex biological systems, multiple perturbation frequencies may be required to explore all of the modes of interest. An optimal frequency of perturbation exists for identification of the inherent dynamics. Perturbation frequencies below or above the optimum identified models with significant error.

    Dynamic System Identification: Experiment Design and Data Analysis Dynamic System Identification: Experiment Design and Data Analysis
    Dynamic System Identification: Experiment Design and Data Analysis Dynamic System Identification: Experiment Design and Data Analysis
    Dynamic System Identification: Experiment Design and Data Analysis Dynamic System Identification: Experiment Design and Data Analysis
    Dynamic System Identification: Experiment Design and Data Analysis Dynamic System Identification: Experiment Design and Data Analysis
    Dynamic System Identification: Experiment Design and Data Analysis Dynamic System Identification: Experiment Design and Data Analysis
    Dynamic System Identification: Experiment Design and Data Analysis Dynamic System Identification: Experiment Design and Data Analysis
    Dynamic System Identification: Experiment Design and Data Analysis Dynamic System Identification: Experiment Design and Data Analysis

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