Technical Brief

Adaptive Tracking in Mobile Robots With Input-Output Linearization

[+] Author and Article Information
Cesáreo Raimúndez

Associate Professor
Nonlinear Control Group,
Department of Systems & Control Engineering,
University of Vigo,
Vigo 36310, Spain
e-mail: cesareo@uvigo.es

Alejandro F. Villaverde

Bioprocess Engineering Group,
Department of Food Technology,
Vigo 36208, Spain
e-mail: afvillaverde@iim.csic.es

Antonio Barreiro

Nonlinear Control Group,
Department of Systems & Control Engineering,
University of Vigo,
Vigo 36310, Spain
e-mail: abarreiro@uvigo.es

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received January 27, 2011; final manuscript received March 19, 2014; published online July 9, 2014. Assoc. Editor: Nader Jalili.

J. Dyn. Sys., Meas., Control 136(5), 054503 (Jul 09, 2014) (7 pages) Paper No: DS-11-1018; doi: 10.1115/1.4027369 History: Received January 27, 2011; Revised March 19, 2014

This paper presents a neural network adaptive controller for trajectory tracking of nonholonomic mobile robots. By defining a point to follow (look-ahead control), the path-following problem is solved with input-output linearization. A computed torque plus derivative (PD) controller and a dynamic inversion neural network controller are responsible for reducing tracking error and adapting to unmodeled external perturbations. The adaptive controller is implemented through a hidden layer feed-forward neural network, with weights updated in real time. The stability of the whole system is analyzed using Lyapunov theory, and control errors are proven to be bounded. Simulation results demonstrate the good performance of the proposed controller for trajectory tracking under external perturbations.

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Fig. 1

Two-wheeled mobile robot

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Fig. 2

Controller diagram

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Fig. 3

NN-adaptive element structure

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Fig. 4

Parametric and external perturbations

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Fig. 5

8-shaped path tracking with PD control without perturbation

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Fig. 6

8-shaped path tracking with PD control with perturbation

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Fig. 7

8-shaped path tracking with PD + νad control and perturbation

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Fig. 8

Evolution of the weights, W(t)



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