MATLAB Code Implementation for System Simulation and Identification

This text discusses system simulation and identification, including Recursive Least Squares (RLS) and current advanced identification theories. These methods are particularly suitable for system simulation applications. System simulation is a highly valuable technology that enables effective modeling and prediction of various real-world scenarios. Through system simulation, we can better understand and analyze the performance of different systems, including mechanical, electronic, communication, and other types of systems. Furthermore, system simulation helps optimize the design of various systems, making them more efficient and reliable. During the system simulation process, identification and parameter estimation constitute crucial components. The Recursive Least Squares (RLS) algorithm and advanced identification theories enable more accurate estimation of system parameters, thereby enhancing the precision and reliability of system simulations.

In MATLAB implementation, system identification typically involves using functions from the System Identification Toolbox, such as iddata() for organizing input-output data and pem() for prediction error minimization. The RLS algorithm can be implemented through recursive updating of covariance matrices and parameter estimates using weight factors that prioritize recent data. Advanced identification theories may incorporate neural network approaches using nlarx() for nonlinear systems or subspace methods like n4sid() for state-space model identification. Simulation can be performed using lsim() for continuous systems or sim() for discrete models, with parameter tuning through optimization algorithms like fmincon() for constrained optimization scenarios.