Safety-Regulated Reinforcement Learning Compensated Model Predictive Control for Ground Vehicles with Unknown Disturbances Department of Automotive Engg, Clemson University, Greenvill

Format:

Book

Conference/Event

Author/Creator:

Gupta, Prakhar, author.

Contributor:

Jia, Yunyi

Conference Name:

2024 NDIA Michigan Chapter Ground Vehicle Systems Engineering and Technology Symposium (2024-08-13 : Novi, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

This paper proposes an MPC-RL-CBF control framework that leverages the individual strengths of MPC (Model Predictive Control) schemes and Deep RL (Reinforcement Learning) techniques. This allows using a model mismatched computationally inexpensive optimal controller with a compensating learning technique to handle the uncertainties in system dynamics and unknown external disturbances. The controller is evaluated in simulation for a vehicle tracking a path with a lane change, subjected to unknown crosswinds. The results show that the MPC-RL-CBF approach helps track the path better than the purely model-based approach and does so safely, through safety guided training. This framework can be extended to off-road driving controls under changing terrain types and properties, tire-terrain interaction behavior, steep slopes et cetera

Notes:

Vendor supplied data

Publisher Number:

2024-01-4075

Access Restriction:

Restricted for use by site license