Robust Model Predictive Control Design

Veselý, Vojtech; Rosinov, Danica

doi:10.5772/46952

Cited by 1 publication

(1 citation statement)

References 20 publications

(33 reference statements)

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“…Several robust predictive formulations utilize the min-max approach, where the manipulated input trajectory is computed by solving an optimization problem that requires minimizing the objective function and satisfying the input and state constraints over all possible realizations of the uncertainty. These have been applied mainly to impulse response models and state-space approaches by solving a finite horizon open-loop control optimization problem [4]. Due to its advantages, MPC has been implemented in several applications for automotive and other transportation active safety systems, such as active steering, active traction, active braking, and active differentials or suspension systems, in order to coordinate and improve vehicle handling, stability, and ride comfort [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Study of Model Predictive Control for Path-Following Autonomous Ground Vehicle Control under Crosswind Effect

Yakub

Abu

Sarip

et al. 2016

Journal of Control Science and Engineering

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We present a comparative study of model predictive control approaches of two-wheel steering, four-wheel steering, and a combination of two-wheel steering with direct yaw moment control manoeuvres for path-following control in autonomous car vehicle dynamics systems. Single-track mode, based on a linearized vehicle and tire model, is used. Based on a given trajectory, we drove the vehicle at low and high forward speeds and on low and high road friction surfaces for a double-lane change scenario in order to follow the desired trajectory as close as possible while rejecting the effects of wind gusts. We compared the controller based on both simple and complex bicycle models without and with the roll vehicle dynamics for different types of model predictive control manoeuvres. The simulation result showed that the model predictive control gave a better performance in terms of robustness for both forward speeds and road surface variation in autonomous path-following control. It also demonstrated that model predictive control is useful to maintain vehicle stability along the desired path and has an ability to eliminate the crosswind effect.

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