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Online estimation algorithms estimate the parameters of a model when new data is available during the operation of the model. Consider a heating/cooling system that does not have prior information about the environment in which it operates. Suppose that this system must heat/cool a room to achieve a desired temperature in a given amount of time. To fulfil its objective, the system must obtain knowledge of the temperature and insulation characteristics of the room. You can estimate the insulation characteristics of the room while the heating/cooling system is online (operational). For this estimation, use the system effort as the input and the room temperature as the output. You can feed the estimated model to the heating/cooling system to govern its behavior so that it achieves its objective. Online estimation is ideal for estimating small deviations in the parameter values of a system at a known operating point.
Online estimation is typically performed using a recursive algorithm. To estimate the parameter values at a time step, recursive algorithms use the current measurements and previous parameter estimates. Therefore, recursive algorithms are efficient in terms of memory storage. Additionally, recursive algorithms have smaller computational demands. This efficiency makes them well suited to online and embedded applications.
Common applications of online estimation include:
Adaptive control — Estimate a plant model to modify the controller based on changes in the plant model.
Fault detection — Compare the online plant model with the idealized/reference plant model to detect a fault (anomaly) in the plant.
Soft sensing — Generate a "measurement" based on the estimated plant model, and use this measurement for feedback control or fault detection.
Verify the experiment-data quality before starting offline estimation — Before using the measured data for offline estimation, perform online estimation for a small number of iterations. The online estimation provides a quick check of whether the experiment used excitation signals that captured the relevant system dynamics. Such a check is useful because offline estimation can be time intensive.
The System Identification Toolbox™ software provides the Recursive Least Squares Estimator and Recursive Polynomial Model Estimator blocks to perform online estimation using Simulink^{®}. You can also estimate a state-space model online from these models by using the Recursive Polynomial Model Estimator and Model Type Converter blocks together. You can generate C/C++ code and Structured Text for these blocks using products such as Simulink Coder™ and Simulink PLC Coder™. Use the generated code to deploy online estimation to an embedded target. For example, you can estimate the insulation characteristics of a room from measured input-output data and feed them to the controller for the heating/cooling system. After validating the online estimation in simulation, you can generate code for your Simulink model and deploy the same to the target hardware.
When you perform online estimation using the Recursive Least Squares Estimator and Recursive Polynomial Model Estimator blocks, the following requirements apply:
Model must be discrete-time linear or nearly linear with parameters that vary slowly with respect to time.
Structure of the estimated model must be fixed while the Simulink model is running.
[1] Ljung, L. System Identification: Theory for the User. Upper Saddle River, NJ: Prentice-Hall PTR, 1999, pp. 428–440.
Kalman Filter | Recursive Least Squares Estimator | Recursive Polynomial Model Estimator