Nonlinear stochastic predictive control with unscented transformation for semi-autonomous vehicles

This paper presents a novel predictive control approach based on the unscented transformation with recursive feasibility analysis and an experimental validation for lane keeping of semi-autonomous vehicles. The optimization problem to be solved is nonlinear with stochastic disturbances and probabili...

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Bibliographic Details
Published in2014 American Control Conference pp. 5574 - 5579
Main Authors Changchun Liu, Gray, Andrew, Chankyu Lee, Hedrick, J. Karl, Jiluan Pan
Format Conference Proceeding
LanguageEnglish
Published American Automatic Control Council 01.06.2014
Subjects
Automotive
Computational modeling
Nonlinear systems
Optimization
Predictive control for nonlinear systems
Predictive models
Stochastic processes
Trajectory
Vehicles
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Summary:This paper presents a novel predictive control approach based on the unscented transformation with recursive feasibility analysis and an experimental validation for lane keeping of semi-autonomous vehicles. The optimization problem to be solved is nonlinear with stochastic disturbances and probability constraints on states. The unscented transformation is utilized to calculate the propagation of disturbed states over the prediction horizon, and the probability constraints are transformed into constraint functions with Chebyshev's inequality. A sufficient condition for recursive feasibility is proved by considering the worst case of the disturbance realization. Experiments on the lane keeping system with an uncertain driver model validate the effectiveness of the proposed approach.
ISBN:1479932728
9781479932726
ISSN:0743-1619
2378-5861
DOI:10.1109/ACC.2014.6859347