Online System Identification Algorithm without Persistent Excitation for Robotic Systems: Application to Reconfigurable Autonomous Vessels

Abstract

This paper investigates an online system identification problem of estimating unknown parameters in nonlinear system dynamics in the absence of persistently excitation. To estimate parameters, we develop an algorithm that updates parameter estimates using sensor data and a basis that is built on a finite number of recorded sensor data. Based on our proposed approach we show that the algorithm achieves exponential convergence in both state and parameter estimation errors without the persistent excitation condition. We demonstrate the effectiveness of the proposed approach using both simulations and experiments on a reconfiguration autonomous multi-vessel platform: Simulation results illustrate that the parameter estimated by the developed algorithm converge to their ground truths. Experiment results validate the performance of the developed algorithm in estimating platform's system parameters across different multi-vessel configurations.