Nonlinear Model Predictive Path-Following Control for Highly Automated Driving

Ritschel, Robert; Schrödel, Frank; Hädrich, Juliane; Jäkel, Jens

doi:10.1016/j.ifacol.2019.08.112

Cited by 24 publications

(21 citation statements)

References 7 publications

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“…The proposed approach for optimization of weighting parameters is demonstrated using the MPC realization of [8]. This MPC is designed for longitudinal and lateral vehicle guidance in the context of highly automated driving.…”

Section: A Model Predictive Path-following Controlmentioning

confidence: 99%

Multi-Objective Optimization of a Path-following MPC for Vehicle Guidance: A Bayesian Optimization Approach

Gharib¹,

Stenger²,

Ritschel³

et al. 2021

Preprint

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This paper tackles the multi-objective optimization of the cost functional of a path-following model predictive control for vehicle longitudinal and lateral control. While the inherent optimal character of the model predictive control and the direct consideration of constraints gives a very powerful tool for many applications, is the determination of an appropriate cost functional a non-trivial task. This results on the one hand from the number of degrees of freedom or the multitude of adjustable parameters and on the other hand from the coupling of these. To overcome this situation a Bayesian optimization procedure is present, which gives the possibility to determine optimal cost functional parameters for a given desire. Moreover, a Pareto-front for a whole set of possible configurations can be computed.

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Section: A Model Predictive Path-following Controlmentioning

confidence: 99%

Multi-Objective Optimization of a Path-following MPC for Vehicle Guidance: A Bayesian Optimization Approach

Gharib¹,

Stenger²,

Ritschel³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The switching condition is based on a physical measure usually available on the vehicle's acquisition, v x being the most used [28]. However, this value is typically defined by the designer by a rule of thumb based on several tests.…”

Section: Tuning Procedures For Model Blendingmentioning

confidence: 99%

“…This way, some authors have integrated both vehicle models in parallel to estimate more accurately relevant vehicle dynamics behavior, such as the side-slip angle [25] or the vehicle's position [26,27]. As the previous technique requires computing both models in parallel and increasing the computational effort, in recent years, the so-called model-blending approach has been proposed by some authors [28,29]. In this latter method, a model-switching strategy allows for selecting the most appropriate model depending on the driving scenario, allowing for increasing the validity range of the MPC-based vehicle control approach.…”

Section: Introductionmentioning

confidence: 99%

“…This criterion has to be defined using a variable that allows for optimizing the operative range of each model so that the model-blending effectively improves trajectory tracking. In [28,29], the longitudinal speed is used as the switching condition, selecting the kinematic model to compute MPC predictions when the vehicle moves at low speed while using the dynamic model when moving at high-speed. However, as model validity is limited by the tire model saturation, it seems more appropriate to use another variable to perform the switching which is directly related to the tires' forces, such as lateral acceleration.…”

Section: Introductionmentioning

confidence: 99%

“…However, as model validity is limited by the tire model saturation, it seems more appropriate to use another variable to perform the switching which is directly related to the tires' forces, such as lateral acceleration. Regarding the switching mode, previous works have presented two different methods-firstly, a sudden switch strategy that instantly changes between kinematic and dynamic models [28]; secondly, a progressive switching strategy that performs this change more smoothly using a linear approach [29]. Nevertheless, there is not a recommended procedure to blend models, besides the trial-and-error method.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Lateral-Acceleration-Based Vehicle-Models-Blending for Automated Driving Controllers

et al. 2020

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Model-based trajectory tracking has become a widely used technique for automated driving system applications. A critical design decision is the proper selection of a vehicle model that achieves the best trade-off between real-time capability and robustness. Blending different types of vehicle models is a recent practice to increase the operating range of model-based trajectory tracking control applications. However, current approaches focus on the use of longitudinal speed as the blending parameter, with a formal procedure to tune and select its parameters still lacking. This work presents a novel approach based on lateral accelerations, along with a formal procedure and criteria to tune and select blending parameters, for its use on model-based predictive controllers for autonomous driving. An electric passenger bus traveling at different speeds over urban routes is proposed as a case study. Results demonstrate that the lateral acceleration, which is proportional to the lateral forces that differentiate kinematic and dynamic models, is a more appropriate model-switching enabler than the currently used longitudinal velocity. Moreover, the advanced procedure to define blending parameters is shown to be effective. Finally, a smooth blending method offers better tracking results versus sudden model switching ones and non-blending techniques.

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Model Predictive Path Following Control without terminal constraints for holonomic mobile robots

Cenerini

Mehrez

Han

et al. 2023

Control Engineering Practice

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Nonlinear Model Predictive Path-Following Control for Highly Automated Driving

Cited by 24 publications

References 7 publications

Multi-Objective Optimization of a Path-following MPC for Vehicle Guidance: A Bayesian Optimization Approach

Multi-Objective Optimization of a Path-following MPC for Vehicle Guidance: A Bayesian Optimization Approach

Lateral-Acceleration-Based Vehicle-Models-Blending for Automated Driving Controllers

Model Predictive Path Following Control without terminal constraints for holonomic mobile robots

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