A Generalized Recursive Identification Algorithm Compensated by Orthogonal Weighted Kernel for Tracking Time-Variant Systems

Abstract

The main purpose of this paper is to introduce a generalized recursive identification algorithm compensated by orthogonal weighted kernel. The performance index of the novel proposed algorithm considers information about both the estimation error and first time derivative of estimation error. The estimation process is implemented in a recursive sliding window scheme. In addition, linear combinations of sine and cosine basis functions, which lead to Fourier orthogonal series, are employed as a kernel to approximate the time-varying parameters as continuous functions in each sliding window. Properties of the proposed algorithm are extended to both least squares estimator and instrumental variable estimator; thus, it is applicable to systems with correlated and uncorrelated noise. Simulation results demonstrate the efficiency of the proposed algorithm for accurate and online tracking of unknown parameters’ trend.