NAME: identification expand the application field beyond the scope

NAME: ISHMAEL ALFREDID: 14002264MEASUREMENT AND INSTRUMENTATIONHISTORYMOTIVATION APPLICATION OF SYSTEM IDENTIFICATIONSystem identification in a nutshell….It is basically the task of precisely constructing a dynamic modelthat can be used to predict the outputs of a dynamical system.

Models ofdynamic systems are typically described by differential or differenceequations, transfer functions, state-space equations, and pole-zero-gainmodels.HISTORYAccording to the IEEE Control Systems Magazine, early work in thefield of system identification was started or developed by the statistics andtime series communities. It can widely be seen or portrayed in the work ofFisher (1912), Gauss (1809) and the theory of stochastic processes.The start of the model based control era was around the 1960’swith the help of Kalman’s key papers. This lead to the development of model basedtheory for prediction, filtering and control. A growing pressure to applymodern techniques to areas where models are not available from physics was needthus the Need for system identification.

IMPORTANCEOF SYSTEM IDENTIFICATIONIt is mostly used to estimate a grey or black model of a system whichis dynamic based on observing the input-output from experimental data.Thus, system identification can be used to solve problems indiverse fields because reliability and availability of the design techniques ofsystem identification expand the application field beyond the scope ofindustrial applications.So, system identification helps to:·        Design control strategies for a system afteridentification, example can be in optimizing an electrical microgrid operation.

·        Forecast the evolution for a particular system(e.g. the future climate prediction according a to IPCC downscaling model).

·        Improve the internal knowledge of the systemwhich is to be identified.·        Distinguish concealed components impacting asystem for example sun spots in the karst spring.·        To recognize the interaction between coupledsystems (for example glaciers and climate).·        Learn and analyse the properties of anidentified system.System identification techniques·        Maximum likelihood·        Prediction-error minimization (PEM)·        Subspace system identificationSYSTEM IDENTIFICATION AND MATLABIt is possible to create linear andnonlinear dynamic system models from measured input or output data with thehelp of SystemIdentification Toolbox™ in MATLAB.The system identification toolbox provides Simulink blocks as wellas MATLAB functions and an application for constructing mathematical models ofdynamic systems. It gives you a chance to make and utilize models of dynamicframeworks not effectively displayed from first standards or details. Youcan utilize time-area and recurrence space input-yield information todistinguish constant time and discrete-time exchange capacities, processmodels, and state-space models.

The toolbox additionally gives algorithms forembedded online parameter estimation.The toolbox can also perform grey-box identification forestimating parameters of a client characterized model. System responseprediction and plant modelling of the identified model can be used in Simulink.The toolbox supports time-series forecasting and time-series data modelling.APPLICATIONSOF SYSTEM IDENTIFICATIONThere are basically two different applications of systemidentification which are control and analysis.

Control – in system identification you would want to know thedynamics of the plan and you would want to design a controller to improve theperformance of the plan or system.Analysis – the analysis part is basically aims to understand thefunctioning of the plan or system·        Predicting future climate change-Researchers have built up a few PC runrecreations, or models, that consolidate and express in mathematical form whatwe know about the procedures that control the atmospheric and hydrologic frameworks.The models used are called General Circulation Models(GCM). These models are alsoused by scientists to try and predict the effects or impacts of increasedgreenhouse gas concentration in the atmosphere.·        Prediction of energy consumption in buildings-The models used will predict the systemsperformance in terms of energy consumption using the measured input and output.

To train and test the models, data is taken or acquired from existing buildings.Nonlinear, State space, and polynomials models based mathematical functions aretested with different parameters such are temperature, time, and dewpoint.  The outcomes demonstrate that the proposed models can yield oroutput similar energy results. The created model can be utilized for vitalityevaluation and analysis. System identification Process1.      Experiment planning2.

      Selection of model procedure3.      Parameter Estimation4.      Model validation REFERENCES