An Online Scheme for Delayed MISO System Identification

Abstract

The issue of parametric estimation in time delay models is the main topic of this research article. Multiple-Input Single-Output (MISO) Continuous-Time (CT) systems with numerous unknown time delays characterize these models. Two different recursive parametric estimate techniques are explored in this paper, the strategic application of Sequential Nonlinear Least Squares (SNLS) to attain global convergence and the Recursive Least Squares (RLS) technique in conjunction with the Gauss-Newton algorithm with the goal of achieving local optimization. Both approaches contribute to the comprehensive understanding of the parametric estimation landscape for time delay models. In a pivotal stride towards enhancing convergence, the research proposes a hybridization of the two methods. This synergistic approach is designed to leverage the strengths of both SNLS and the RLS-Gauss-Newton combination, fostering improved overall convergence properties. To substantiate the credibility and effectiveness of the proposed methodologies, the conducted research provides comprehensive simulation results. These simulations offer concrete examples of the efficacy and practicality of the suggested techniques in real-world situations, and they make significant contributions to the field of parametric estimation for time delay models.