MATLAB Examples

The effect of intersample behavior on the estimation of continuous-time models using discrete-time frequency-response data.

Convert a frequency-response data (FRD) model to a transfer function model. You treat the FRD model as estimation data and then estimate the transfer function.

Grey-box modeling of the dynamics of an industrial robot arm. The robot arm is described by a nonlinear three-mass flexible model according to Figure 1. This model is idealized in the sense

The modeling of a measured signal. We analyze the current signal from the R-phase when a 400 kV three-phase transformer is energized. The measurements were performed by Sydkraft AB in

Estimate a transfer function from frequency response data. You use Simulink® Control Design™ to collect frequency response data from a Simulink model and the tfest command to estimate a

Develop and analyze simple models from a real laboratory process data. We start with a small description of the process, learn how to import the data to the toolbox and preprocess/condition

Nonlinear grey-box modeling of vehicle dynamics. Many new vehicle features (like Electronic Stability Programs (ESP), indirect Tire Pressure Monitoring Systems (TPMS), road-tire

Nonlinear black-box modeling of the dynamic behavior of a magneto-rheological fluid damper. It shows how to create Nonlinear ARX and Hammerstein-Wiener models of the damper using

Estimate a transfer function from measured signal data.

The grey-box modeling of a large and complex nonlinear system. The purpose is to show the ability to use the IDNLGREY model to estimate a large number of parameters (16) in a system having many

Linear model identification of a glass tube manufacturing process. The experiments and the data are discussed in:

Computation of bending modes of a flexible wing aircraft. The vibration response of the wing is collected at multiple points along its span. The data is used to identify a dynamic model of the

Create a periodic random Gaussian input signal using idinput.

Create a multi-experiment, time-domain data set by merging only the accurate data segments and ignoring the rest.

Generate output data by simulating a model using an input signal created using idinput .

Create a multiexperiment iddata object by merging iddata objects, where each contains data from a single experiment or is a multiexperiment data set.

Create a time series model and use the model for prediction, forecasting, and state estimation. The measured data is from an induction furnace whose slot size erodes over time. The slot size

Use a data-based modeling approach for fault detection. This example requires Statistics and Machine Learning Toolbox™.

Use an extended Kalman filter for fault detection. The example uses an extended Kalman filter for online estimation of the friction of a simple DC motor. Significant changes in the estimated

Detect abrupt changes in the behavior of a system using online estimation and automatic data segmentation techniques.

Perform multivariate time series forecasting of data measured from predator and prey populations in a prey crowding scenario. The predator-prey population-change dynamics are modeled

Estimate the states of a nonlinear system using an Unscented Kalman Filter in Simulink™. The example also illustrates how to develop an event-based Kalman Filter to update system

Extract features from vibration signals from a ball bearing, conduct health monitoring, and perform prognostics. This example requires Signal Processing Toolbox?.

Estimate the unknown parameters of a continuous-time model.

Access the estimation report.

Estimate model parameters using linear and nonlinear grey-box modeling.

Represent the structure of the following continuous-time model:

Estimate parameters in user-defined model structures. Such structures are specified by IDGREY (linear state-space) or IDNLGREY (nonlinear state-space) models. We shall investigate

Illustrates how models simulated in Simulink® can be identified using System Identification Toolbox™. The example describes how to deal with continuous-time systems and delays, as well

Manage data and model objects available in the System Identification Toolbox™. System identification is about building models from data. A data set is characterized by several pieces of

Obtain linear approximations of a complex, nonlinear system by means of linear model identification. The approach is based on selection of an input signal that excites the system. A linear

The benefits of regularization for identification of linear and nonlinear models.

Estimate models using frequency domain data. The estimation and validation of models using frequency domain data work the same way as they do with time domain data. This provides a great

Build simple process models using System Identification Toolbox™. Techniques for creating these models and estimating their parameters using experimental data is described. This

Some methods for choosing and configuring the model structure. Estimation of a model using measurement data requires selection of a model structure (such as state-space or transfer

Perform and troubleshoot the identification of a SISO system using frequency-response data (FRD). The techniques explained here can also be applied to MIMO models and frequency-domain

Estimate ARMAX and OE-form models using the state-space estimation approach.

Several identification methods available in System Identification Toolbox™. We begin by simulating experimental data and use several estimation techniques to estimate models from the

Estimate the unknown parameters of a discrete-time model.

Identify a transfer function containing a specified number of poles for given data.

Estimate a process model with fixed parameters.

Perform spectral estimation on time series data. We use Marple's test case (The complex data in L. Marple: S.L. Marple, Jr, Digital Spectral Analysis with Applications, Prentice-Hall,

Estimate a transfer function model with unknown transport delays and apply an upper bound on the unknown transport delays.

Identify a transfer function to fit a given frequency response data (FRD) containing additional phase roll off induced by input delay.

Estimate transfer function models with I/O delays.

Deal with data with several input and output channels (MIMO data). Common operations, such as viewing the MIMO data, estimating and comparing models, and viewing the corresponding model

Forecast time series data from a system using an ARMA model. Load the time series data that is to be forecasted.

Create input data and a model, and then use the data and the model to simulate output data.

Simulate a continuous-time state-space model using a random binary input u and a sample time of 0.1 s .

Manually reproduce forecasting results that are obtained using the forecast command. You first use the forecast command to forecast time series data into the future. You then compare the

Validate an estimated model by comparing the simulated model output with measured data.

Refine models for which you have initial parameter guesses.

Estimate an initial model and refine it using pem .

Use custom regressors in nonlinear ARX (IDNLARX) models, including single-input-single-output (SISO) and multi-input-multi-output (MIMO) systems.

Identify single-input-single-output (SISO) nonlinear black box models using measured input-output data. The example uses measured data from a two-tank system to explore various model

Estimate multi-input multi-output (MIMO) nonlinear black box models from data. Two types of nonlinear black box models are offered in the toolbox - Nonlinear ARX and Hammerstein-Wiener

Design C-MEX model files that involve scalar, vector as well as matrix parameters. As a modeling basis, we will use a somewhat idealized industrial robot, where the left-hand sides of the

Identify the parameters of a complex yet artificial nonlinear discrete-time system with one input and one output. The system was originally proposed and discussed by Narendra and Li in the

Include and simulate an IDNLGREY model in Simulink®. We use a chemical reaction system as a modeling basis. The first modeling and identification part of the example can be run without

Estimate parameters of a nonlinear grey box model using multiple experiment data. A system exhibiting dry friction between two solid bodies will be used as the basis for the discussion. In

Provide optional input arguments to IDNLGREY models. The discussion concentrates on how to do this for C-MEX types of model files, yet to some minor extent we will also address the most

How the estimation algorithm choices may impact the results for a nonlinear grey box model estimation. We use data produced by a nonlinear pendulum system, which is schematically shown in

Perform IDNLGREY modeling based on C MEX model files. It uses a simple system where nonlinear state space modeling really pays off.

Construct, estimate and analyze nonlinear grey-box models.

Write ODE files for nonlinear grey-box models as MATLAB and C MEX files.

Grey-box modeling of a static single-input, single-output system using a MATLAB function as the ODE file.

Create nonlinear grey box time series models. Time series models are models not using any measured inputs. Three idealized ecological systems are studied where two species either:

Estimate Hammerstein-Wiener models using linear OE models.

Use nlarx to estimate a nonlinear ARX model for measured input/output data.

Estimate and compare multiple Hammerstein-Wiener models using measured input-output data.

How the software evaluates the simulated output by first computing the output of the input and output nonlinearity estimators.

Use the Hammerstein-Wiener model structure to improve a previously estimated linear model.

Estimate nonlinear ARX models by using linear ARX models.

Estimate a Hammerstein-Wiener model and then use nlhw command to iteratively refine the model.

Estimate states of linear systems using time-varying Kalman filters in Simulink. You use the Kalman Filter block from the System Identification Toolbox/Estimators library to estimate

Implement an online polynomial model estimator. You estimate two ARMAX models for a nonlinear chemical reaction process. These models capture the behavior of the process at two operating

Perform online parameter estimation for line-fitting using recursive estimation algorithms at the MATLAB command line. You capture the time-varying input-output behavior of the

Perform online parameter estimation for a time-varying ARX model at the MATLAB command line. The model parameters are updated at each time step with incoming new data. This model captures

Implement an online recursive least squares estimator. You estimate a nonlinear model of an internal combustion engine and use recursive least squares to detect changes in engine inertia.

Use the unscented Kalman filter and particle filter algorithms for nonlinear state estimation for the van der Pol oscillator.

Perform nonlinear state estimation in Simulink™ for a system with multiple sensors operating at different sample rates. The Extended Kalman Filter block in System Identification

Demonstrates the use of Particle Filter block in System Identification Toolbox™. A discrete-time transfer function parameter estimation problem is reformulated and recursively solved

Estimate the states of a discrete-time Van der Pol oscillator and compute state estimation errors and residuals for validating the estimation. The residuals are the output estimation

Analyze time-series models.

Estimate Autoregressive Integrated Moving Average or ARIMA models.

Simulate a time-series model, compare the spectral estimates, estimate covariance, and predict output of the model.

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