This paper presents a hierarchical framework with novel analytical and neural physics-driven models, to enable the online planning and tracking of minimum-time maneuvers, for a vehicle with partially-unknown parameters. We introduce a lateral speed prediction model for high-level motion planning with economic nonlinear model predictive control (E-NMPC). A low-level steering controller is developed with a novel feedforward-feedback physics-driven artificial neural network (NN). A longitudinal dynamic model is identified to tune a low-level speed-tracking controller. The high- and low-level control models are identified with an automatic three-step scheme, combining open-loop and closed-loop maneuvers to model the maximum acceleration G-G-v performance constraint for E-NMPC, and to capture the effect of the longitudinal acceleration on the lateral dynamics. The proposed framework is used in a simulation environment, for the online closed-loop control of a highly detailed sedan vehicle simulator, whose parameters are partially-unknown. Two different circuits are adopted to validate the approach, and a robustness analysis is performed by varying the vehicle mass and the load distribution. A minimum-time optimal control problem is solved offline and used for a comparison with the closed-loop results. A video demonstrating both the automatic three-step identification scheme and the motion planning and control results is available at the following link: https://www.youtube.com/watch?v=xQ_T96IjGP8.

A Physics-Driven Artificial Agent for Online Time-Optimal Vehicle Motion Planning and Control / Piccinini, Mattia; Taddei, Sebastiano; Larcher, Matteo; Piazza, Mattia; Biral, Francesco. - In: IEEE ACCESS. - ISSN 2169-3536. - 11:(2023), pp. 46344-46372. [10.1109/ACCESS.2023.3274836]

A Physics-Driven Artificial Agent for Online Time-Optimal Vehicle Motion Planning and Control

Piccinini, Mattia
Primo
;
Taddei, Sebastiano
Secondo
;
Larcher, Matteo;Piazza, Mattia
Penultimo
;
Biral, Francesco
Ultimo
2023-01-01

Abstract

This paper presents a hierarchical framework with novel analytical and neural physics-driven models, to enable the online planning and tracking of minimum-time maneuvers, for a vehicle with partially-unknown parameters. We introduce a lateral speed prediction model for high-level motion planning with economic nonlinear model predictive control (E-NMPC). A low-level steering controller is developed with a novel feedforward-feedback physics-driven artificial neural network (NN). A longitudinal dynamic model is identified to tune a low-level speed-tracking controller. The high- and low-level control models are identified with an automatic three-step scheme, combining open-loop and closed-loop maneuvers to model the maximum acceleration G-G-v performance constraint for E-NMPC, and to capture the effect of the longitudinal acceleration on the lateral dynamics. The proposed framework is used in a simulation environment, for the online closed-loop control of a highly detailed sedan vehicle simulator, whose parameters are partially-unknown. Two different circuits are adopted to validate the approach, and a robustness analysis is performed by varying the vehicle mass and the load distribution. A minimum-time optimal control problem is solved offline and used for a comparison with the closed-loop results. A video demonstrating both the automatic three-step identification scheme and the motion planning and control results is available at the following link: https://www.youtube.com/watch?v=xQ_T96IjGP8.
2023
Piccinini, Mattia; Taddei, Sebastiano; Larcher, Matteo; Piazza, Mattia; Biral, Francesco
A Physics-Driven Artificial Agent for Online Time-Optimal Vehicle Motion Planning and Control / Piccinini, Mattia; Taddei, Sebastiano; Larcher, Matteo; Piazza, Mattia; Biral, Francesco. - In: IEEE ACCESS. - ISSN 2169-3536. - 11:(2023), pp. 46344-46372. [10.1109/ACCESS.2023.3274836]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/392730
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