Factors limiting Identification

Factors limiting Identification Available measurement time is always limited. The maximum allowable change of input signal is always limited. The maximum allowable change in the output may also be limited. Linearity assumption may be valid only for the small region of operation. Disturbance signal having different components.example- deterministic, stochastic or completely unknown.

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Extended Kalman Filter | Algorithm & Applications

In the previous article, we had learned about Kalman filter. As Kalman filter assumes linear system but finds greatest applications in non-linear systems. Kalman filter assumes an approximate solution, describe the deviations from the reference by linear equations. Kalman filter has issues of divergence also. When the error covariance matrix Pk computed by the Kalman filter […]

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Kalman Filter | Algorithm & Applications

The Kalman filter is a recursive state space model based estimation algorithm. In other words, it is an optimal recursive data processing algorithm. Kalman filter is also called as the Predictor-Corrector algorithm. This filter is named after Rudolph E. Kalman, who in 1960 published his famous paper describing a recursive solution to the discrete data […]

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Parametric and Non-Parametric Models

In this article, we will learn the basic difference between Parametric and Non-Parametric Models Parametric Models It represents the relation between input and output by means of equations. It contains the finite number of explicit parameters. Model coefficients are not known exactly. White box models are the parametric models. For example: differential equations in mathematical […]

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Types of Identification

There are 3 types of Identification Deterministic Identification Stochastic Identification Complete system Identification 1. Deterministic Identification The process noise v(t) is absent and measurement noise w(t) is present. We estimate system output y(t). Find the relation between input u(t) and output y(t). Measurement of input u(t) and measured output z(t) is given.   2. Stochastic […]

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Identification Definition & Model Types

System The complex interaction between diverse components is called as System. System Variables A state variable is one of the set of variables that are used to describe the mathematical “state” of a dynamical system. State variables are used to represent the states of a general system. example: In electrical circuits, the node voltages and the […]

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Difference between Theoretical Modelling and Identification

The Difference between Theoretical Modelling and Identification is given in the following table. S.No. Theoretical Modelling Identification 1. Model structure follows from laws of nature. Model structure must be assumed. 2. Modelling of input-output behavior as well as the internal behaviour. Only the input-output behaviour is identified. 3. Model parameters are given as function of […]

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Difference between Online Identification and Offline Identification

The Difference between Online Identification and Offline Identification is given in the following table. S.No. Online Identification Offline Identification 1. Used for control applications. Used for Analysis applications. 2. Model is used for controller design. Model is used for offline simulation of systems. 3. Compact and easy to manipulate model. Free run simulation without access […]

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