Dynamic Modeling of a Nonlinear Two-Wheeled Robot Using Data-Driven Approach

Muhammad Aseer Khan,Bilal Ashraf,Junaid Rashid,Dur-E-Zehra Baig,Husan Ali,Jungeun Kim

doi:10.3390/pr10030524

Abstract

A system identification of a two-wheeled robot (TWR) using a data-driven approach from its fundamental nonlinear kinematics is investigated. The fundamental model of the TWR is implemented in a Simulink environment and tested at various input/output operating conditions. The testing outcome of TWR’s fundamental dynamics generated 12 datasets. These datasets are used for system identification using simple autoregressive exogenous (ARX) and non-linear auto-regressive exogenous (NLARX) models. Initially the ARX structure is heuristically selected and estimated through a single operating condition. We conclude that the single ARX model does not satisfy TWR dynamics for all datasets in term of fitness. However, NLARX fitted the 12 estimated datasets and 2 validation datasets using sigmoid nonlinearity. The obtained results are compared with TWR’s fundamental dynamics and predicted outputs of the NLARX showed more than 98% accuracy at various operating conditions.

Highlights

System identification using open loop experimental datasets is a well-known trick for determining the unknown dynamics of a real-time system [1]
The unknown dynamics of a system can be represented in terms of both frequency and time domain, depending upon the control methodology, which determines the type of mathematical representation being adopted
two-wheeled robot (TWR) kinematics is implemented in a Simulink environment using the S function

Summary

Introduction

System identification using open loop experimental datasets is a well-known trick for determining the unknown dynamics of a real-time system [1]. The mathematical models obtained through an open loop experimental data driven approach serve as a bridge between control theory and real-world problems. The development of a modern feedback control system for a real- world problem requires an accurate estimate of unknown dynamics. Good system identification will lead a control designer to develop a control system in a systematic manner and diagnose an appropriate solution to a real-world problem [2,3]. The unknown dynamics of a system can be represented in terms of both frequency and time domain, depending upon the control methodology, which determines the type of mathematical representation being adopted. Classical control theory uses the frequency domain and modern control systems uses state space representation (time domain). System identification using open loop datasets at various operating conditions [4]

Methods

Results

Discussion

Conclusion