System Identification using MATLAB Code Implementation

In this article, we explore the topic of system identification. System identification employs a recursive least squares algorithm method that supports online identification at any time and performs real-time parameter estimation. This approach is particularly important as it helps us better understand and control system behavior, thereby achieving improved results. To implement this method effectively, we need to understand the fundamental principles of recursive algorithms and their practical application scenarios. The MATLAB implementation typically involves key functions like rls or custom recursive functions that update parameter estimates with each new data sample. The algorithm works by continuously minimizing the sum of squared errors between model predictions and actual measurements, with weight updates computed using matrix inversion lemma for computational efficiency. In this article, we will delve into these aspects and introduce readers to practical techniques and technologies that facilitate better application of this method, including code structure organization, parameter tuning strategies, and real-time data handling mechanisms.