Augmented flexible least squares algorithm for time-varying parameter systems

This study proposes an augmented flexible least squares (FLS) algorithm for time-varying parameter systems. The parameter estimates, obtained by minimizing the squared residual measurement and dynamic errors, can catch the true values through a penalized term/weight. The algorithm associated properties are analyzed accordingly. By absorbing all into time varying parameters, the algorithm can convert complex nonlinear processes into various linear relations in time varying parameters. Thus, it can be extended to many kinds of systems. Compared to the classical FLS algorithm, the algorithm proposed in this article has less computational efforts and concise structures. To show the effectiveness of the algorithm and help the readers to follow systematically, this study provides several simulation examples.