Model Predictive Control for Autonomous Driving considering Actuator Dynamics

Babu, Mithun; Theerthala, Raghu Ram; Singh, Arun Kumar; Baladhurgesh, B. P.; Gopalakrishnan, Bharath; Krishna, K. Madhava

doi:10.48550/arxiv.1803.03478

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, an increase has been observed in employing MPC for tasks of robot navigation [21], [22], [23]. The works which used MPC for steering control tasks in autonomous driving include [24], [25]. While the focus in [24] was on the inclusion of actuator dynamics and its effect in overtaking and lane change maneuvres, [25] focused on the task of following a reference trajectory similar to our case.…”

Section: B Controller Synthesismentioning

confidence: 99%

Design and Implementation of Path Trackers for Ackermann Drive based Vehicles

Patnaik,

Patel,

Mohta

et al. 2020

Preprint

View full text Add to dashboard Cite

This article is an overview of the various literature on path tracking methods and their implementation in simulation and realistic operating environments. The scope of this study includes analysis, implementation, tuning, and comparison of some selected path tracking methods commonly used in practice for trajectory tracking in autonomous vehicles. Many of these methods are applicable at low speed due to the linear assumption for the system model, and hence, some methods are also included that consider non-linearities present in lateral vehicle dynamics during high-speed navigation. The performance evaluation and comparison of tracking methods are carried out on realistic simulations and a dedicated instrumented passenger car, Mahindra e2o, to get a performance idea of all the methods in realistic operating conditions and develop tuning methodologies for each of the methods. It has been observed that our model predictive control-based approach is able to perform better compared to the others in medium velocity ranges.

show abstract