Data-Driven Time-Delayed Control for Euler-Lagrange Systems

Abstract

In this paper, a data-driven time-delayed control (TDC) approach is developed for Euler-Lagrange systems. The TDC is a kind of control approaches and can handle model uncertainties, unmodelled dynamics or unknown disturbances in the Euler-Lagrange system conveniently, whose basic idea is to use the measurement information at the last time instant to approximately estimate all the unknown factors at the current time instant. A user specified constant gain matrix is the main design parameter and influences both the closed-loop stability and the tracking performance. Currently this constant gain matrix is either calculated by using the system model based on a stability condition or obtained by trial and error, both of which are rather time-consuming in some practical applications. The data-driven TDC approach developed in this paper can find the constant gain matrix directly from the input and output data without any knowledge of the inertia matrix. The input-to-state stability of the closed-loop system is proven. The proposed approach is characterized by easy implementation and low computational efforts. An experimental example of a robot manipulator is given to illustrate the proposed approach.