System Identification Using Recursive Least Squares Method

Using Recursive Least Squares (RLS) method for system identification is a widely adopted approach. This method requires building models in the Simulink environment and implementing the algorithm through source code. RLS is a mathematical method based on least squares principles, used for estimating system parameters through iterative updates of parameter estimates as new data becomes available. Through RLS implementation, system input and output data can be utilized to identify and optimize system models, typically involving key functions like parameter update equations, covariance matrix manipulation, and forgetting factor implementation for adaptive tracking. The algorithm maintains a covariance matrix that is recursively updated, allowing real-time parameter estimation without storing historical data. Therefore, RLS finds extensive applications in the field of system identification, particularly in adaptive control and real-time parameter estimation scenarios where computational efficiency is crucial.