A Novel Data-Driven Controller Tuning Method for Improving Convergence Performance

This paper presents a data-driven control scheme to iteratively achieve the desired objective criterion with significant improvement of the convergence performance for linear-time-invariant (LTI) single-input-single-output (SISO) systems. The internal iterative behavior between the current parameter and the optimal parameter is firstly analyzed with mathematic expression. And a novel iterative law based on the behavior is proposed, which has the ability to directly seek the optimal parameter that minimizes the objective criterion. Subsequently an unbiased gradient estimation based on the Toeplitz matrix is developed to simplify the practical implementation. The proposed algorithm not only guarantees the parameter converging to the global minimization, but also possesses high convergence rate. Comparative case studies are conducted in both simulation and experiment, which show the basic characteristics of excellent convergence accuracy and convergence rate. The proposed strategy essentially provides a novel data-driven controller tuning method and also could be applied to practical applications.