Online learning of MPC for autonomous racing

Costa, Gabriel B.; Pinho, João; Botto, Miguel Ayala; Lima, Pedro U.

doi:10.1016/j.robot.2023.104469

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Recently, some researchers have conducted a review of model predictive path tracking (PT) control for automated road vehicles [24], considering the following MPC methods for PT control: linear MPC [25,26], linear time-varying MPC [27], linear parameter-varying MPC [28], nonlinear MPC [29], hybrid MPC [30], neural network MPC [31], robust MPC [32], and learning MPC [33]. Some researchers have proposed a learning-based model predictive control (LMPC) algorithm for a Formula Student (FS) autonomous vehicle to improve the dynamic model accuracy of the vehicle [34]. Some researchers have summarized the studies on learning-based MPC, focusing on the following three aspects [35]: (1) learning the system dynamics: taking into account the automatic adjustment of the system dynamic model [36], both during operation and between different operational instances;…”

Section: Introductionmentioning

confidence: 99%

A Helly Model-Based MPC Control System for Jam-Absorption Driving Strategy against Traffic Waves in Mixed Traffic

Li,

Roncoli,

2024

Applied Sciences

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Traffic waves in traffic flow significantly impact road throughput and fuel consumption and may even lead to severe safety issues. Currently, in connected and autonomous environments, the jam-absorption driving (JAD) strategy shows good performance in dissipating traffic waves. However, the previous JAD strategy has mostly focused on wave dissipation without adequately assessing traffic efficiency and safety. To address this gap, an optimal control problem for JAD in mixed traffic is proposed to reduce traffic waves. The prediction model is developed using the car-following model within a model predictive control (MPC) framework. The Helly model is selected for the manual vehicle. This is because the Helly model is a linear model that describes the car-following phenomenon accurately without delay effect. In addition, the objective function of the prediction model considers both traffic safety and efficiency while satisfying mechanical and safety constraints. Simulation results indicate that the proposed methodology can effectively reduce traffic jams and improve traffic performance on a one-lane freeway. The optimal method is more applicable to complex traffic wave scenarios, providing a new perspective for reducing traffic jams on the freeway.

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