An LTV Approach to Identifying Nonlinear Systems-with Application to an RRR-Robot

Abstract

Nonlinear systems are appearing in all engineering applications. Deriving models for these systems is important for instance for prediction and control. The goal of this paper is to estimate models of a class of nonlinear systems, from experimental data. When considering slowly varying setpoints, nonlinear systems can be approximated by linear time-varying models. That is, the nonlinear system is linearised around a trajectory of setpoints. The approach followed in this paper formulates the identification problem of a nonlinear system as an exploration through the relevant range of setpoints, which are identifiable by using tools for linear time-varying systems. This approach is demonstrated on an idealised simulation example, and on a real-life robotic application.