Wheeled Mobile Robot Tracking Control without Velocity Measurement

Wang, Qiang; Tong, Guang; Xing, Xin

doi:10.4028/www.scientific.net/amm.373-375.231

Cited by 3 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tracking control using observers when velocity measurement is not possible has been proposed in some articles. [8][9][10] In fact, observers constitute the backbone of the system that is to be controlled by estimating the uncertain parameters and compensate for modeling errors and noise. 11 The success and superiority of dynamic neural network over static neural network in system identification and control of dynamical systems [12][13][14][15][16][17] motivated us to use it as an observer.…”

Section: Introductionmentioning

confidence: 99%

Mobile robot nonlinear feedback control based on Elman neural network observer

Al-Mutib

Abdessemed

Hedjar

et al. 2015

Advances in Mechanical Engineering

View full text Add to dashboard Cite

This article presents a new approach to control a wheeled mobile robot without velocity measurement. The controller developed is based on kinematic model as well as dynamics model to take into account parameters of dynamics. These parameters related to dynamic equations are identified using a proposed methodology. Input-output feedback linearization is considered with a slight modification in the mathematical expressions to implement the dynamic controller and analyze the nonlinear internal behavior. The developed controllers require sensors to obtain the states needed for the closed-loop system. However, some states may not be available due to the absence of the sensors because of the cost, the weight limitation, reliability, induction of errors, failure, and so on. Particularly, for the velocity measurements, the required accuracy may not be achieved in practical applications due to the existence of significant errors induced by stochastic or cyclical noise. In this article, Elman neural network is proposed to work as an observer to estimate the velocity needed to complete the full state required for the closed-loop control and account for all the disturbances and model parameter uncertainties. Different simulations are carried out to demonstrate the feasibility of the approach in tracking different reference trajectories in comparison with other paradigms.

show abstract