Hibrid Adaptive/EKF Motion Control and Data Fusion for Ground Vehicles with Kinematical and Dynamical Uncertainties

Francesco Maria Raimondi, Maurizio Melluso

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Abstract

This paper considers the motion control problem of ground vehicles with nonholonomic constraints and parametric uncertainties both in the kinematic and in the dynamic model. The presence of uncertainties above is treated using adaptation laws where the Lyapunov's stability of the position and orientation errors is proved. Now, if the feedback signals are position and orientation provided by incremental encoders only, then noises of the odometric sensors above can damage the control in terms of difference between the desired and the actual motion of the vehicle and in terms of performances of the parametric adaptation. So an extended Kalman's filter (EKF) is inserted in the feedback for measuring and reducing the odometric noises above. Based on fusion of data provided by multiple proprioceptive sensors (i.e. incremental encoders, vector compass and position sensor), the EKF estimates the state of the vehicle, i.e. position and orientation, to obtain a filtered feedback signal. The adaptive control and the on-line EKF lead to the hybrid adaptive/EKF control of this paper. The control algorithm efficiency is confirmed through simulation experiments.
Lingua originaleEnglish
pagine (da-a)1483-1490
Numero di pagine8
RivistaWSEAS Transactions on Circuits and Systems
Volume5
Stato di pubblicazionePublished - 2006

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Ground vehicles
Extended Kalman filters
Data fusion
Motion control
Feedback
Sensors
Dynamic models
Kinematics
Uncertainty
Experiments

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cita questo

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title = "Hibrid Adaptive/EKF Motion Control and Data Fusion for Ground Vehicles with Kinematical and Dynamical Uncertainties",
abstract = "This paper considers the motion control problem of ground vehicles with nonholonomic constraints and parametric uncertainties both in the kinematic and in the dynamic model. The presence of uncertainties above is treated using adaptation laws where the Lyapunov's stability of the position and orientation errors is proved. Now, if the feedback signals are position and orientation provided by incremental encoders only, then noises of the odometric sensors above can damage the control in terms of difference between the desired and the actual motion of the vehicle and in terms of performances of the parametric adaptation. So an extended Kalman's filter (EKF) is inserted in the feedback for measuring and reducing the odometric noises above. Based on fusion of data provided by multiple proprioceptive sensors (i.e. incremental encoders, vector compass and position sensor), the EKF estimates the state of the vehicle, i.e. position and orientation, to obtain a filtered feedback signal. The adaptive control and the on-line EKF lead to the hybrid adaptive/EKF control of this paper. The control algorithm efficiency is confirmed through simulation experiments.",
keywords = "Controllers, Mobile robots, nonholonomic wheeled",
author = "Raimondi, {Francesco Maria} and Maurizio Melluso",
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language = "English",
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journal = "WSEAS Transactions on Circuits and Systems",
issn = "1109-2734",
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T1 - Hibrid Adaptive/EKF Motion Control and Data Fusion for Ground Vehicles with Kinematical and Dynamical Uncertainties

AU - Raimondi, Francesco Maria

AU - Melluso, Maurizio

PY - 2006

Y1 - 2006

N2 - This paper considers the motion control problem of ground vehicles with nonholonomic constraints and parametric uncertainties both in the kinematic and in the dynamic model. The presence of uncertainties above is treated using adaptation laws where the Lyapunov's stability of the position and orientation errors is proved. Now, if the feedback signals are position and orientation provided by incremental encoders only, then noises of the odometric sensors above can damage the control in terms of difference between the desired and the actual motion of the vehicle and in terms of performances of the parametric adaptation. So an extended Kalman's filter (EKF) is inserted in the feedback for measuring and reducing the odometric noises above. Based on fusion of data provided by multiple proprioceptive sensors (i.e. incremental encoders, vector compass and position sensor), the EKF estimates the state of the vehicle, i.e. position and orientation, to obtain a filtered feedback signal. The adaptive control and the on-line EKF lead to the hybrid adaptive/EKF control of this paper. The control algorithm efficiency is confirmed through simulation experiments.

AB - This paper considers the motion control problem of ground vehicles with nonholonomic constraints and parametric uncertainties both in the kinematic and in the dynamic model. The presence of uncertainties above is treated using adaptation laws where the Lyapunov's stability of the position and orientation errors is proved. Now, if the feedback signals are position and orientation provided by incremental encoders only, then noises of the odometric sensors above can damage the control in terms of difference between the desired and the actual motion of the vehicle and in terms of performances of the parametric adaptation. So an extended Kalman's filter (EKF) is inserted in the feedback for measuring and reducing the odometric noises above. Based on fusion of data provided by multiple proprioceptive sensors (i.e. incremental encoders, vector compass and position sensor), the EKF estimates the state of the vehicle, i.e. position and orientation, to obtain a filtered feedback signal. The adaptive control and the on-line EKF lead to the hybrid adaptive/EKF control of this paper. The control algorithm efficiency is confirmed through simulation experiments.

KW - Controllers

KW - Mobile robots

KW - nonholonomic wheeled

UR - http://hdl.handle.net/10447/6878

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EP - 1490

JO - WSEAS Transactions on Circuits and Systems

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