Steady-state drifting stabilization of RWD vehicles

In this paper we propose a constrained optimal control architecture for combined velocity, yaw and sideslip regulation for stabilisation of the vehicle near the limit of lateral acceleration using the rear axle electric torque vectoring configuration of an electric vehicle. A nonlinear vehicle and tyre model are used to find reference steady-state cornering conditions and design two model predictive control (MPC) strategies of different levels of fidelity: one that uses a linearised version of the full vehicle model with the rear wheels' torques as the input, and another one that neglects the wheel dynamics and uses the rear wheels' slips as the input instead. After analysing the relative trade-offs between performance and computational effort, we compare the two MPC strategies against each other and against an unconstrained optimal control strategy in Simulink and Carsim environment.

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“…The formulation is similar to the one found in [1,14], where the interested reader can refer to for more details.…”

Section: Vehicle Model and Reference Generationmentioning

confidence: 97%

Rear wheel torque vectoring model predictive control with velocity regulation for electric vehicles

Siampis

Velenis

Longo

2015

Vehicle System Dynamics

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“…if N η then 10 Update all networks λ times; steering angles are applied. Therefore, the steering is limited to a smaller range of [−0.8, 0.8] to prevent rollover.…”

Section: A Formulationmentioning

confidence: 99%

High-Speed Autonomous Drifting With Deep Reinforcement Learning

Cai

Mei

Tai

et al. 2020

IEEE Robot. Autom. Lett.

107

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Drifting is a complicated task for autonomous vehicle control. Most traditional methods in this area are based on motion equations derived by the understanding of vehicle dynamics, which is difficult to be modeled precisely. We propose a robust drift controller without explicit motion equations, which is based on the latest model-free deep reinforcement learning algorithm soft actor-critic. The drift control problem is formulated as a trajectory following task, where the errorbased state and reward are designed. After being trained on tracks with different levels of difficulty, our controller is capable of making the vehicle drift through various sharp corners quickly and stably in the unseen map. The proposed controller is further shown to have excellent generalization ability, which can directly handle unseen vehicle types with different physical properties, such as mass, tire friction, etc.Index Terms-Deep reinforcement learning, deep learning in robotics and automation, racing car, motion control, field robots.• We design a closed-loop controller based on model-free deep RL to control front-wheel drive (FWD) vehicles to drive at high speed (80-128 km/h), and to drift through sharp corners quickly and stably following a reference trajectory, as shown in Fig. 2.

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“…Finally, novel ADAS systems such as Autonomous Drift Control (ADC) [22,27,30], Figure 10, are situated at the highest level. This system has been proposed by the authors with the aim to maximise the vehicle cornering performance on loose surfaces of limited manoeuvrability (e.g., gravel, deep snow [29,31]) and is inspired by previous research works on automated drift control [29,79] and agile manoeuvring [80,81]. [22,27,30].…”

Section: Vehicle Systems: the Role Of Road Frictionmentioning

confidence: 99%

“…Thus, the road grip potential needs to be continuously monitored during free-rolling, coast-down or gentle acceleration events to correct critical variables such as the minimum separation between vehicles depending on the available friction. Moreover, novel drift-based applications not only require an estimation of the maximum road grip potential, but also a more detailed characterisation of the tyre-road friction (e.g., force versus slip curve [3,27,[29][30][31]). …”

Section: Introductionmentioning

confidence: 99%

Road Friction Virtual Sensing: A Review of Estimation Techniques with Emphasis on Low Excitation Approaches

2017

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Abstract:In this paper, a review on road friction virtual sensing approaches is provided. In particular, this work attempts to address whether the road grip potential can be estimated accurately under regular driving conditions in which the vehicle responses remain within low longitudinal and lateral excitation levels. This review covers in detail the most relevant effect-based estimation methods; these are methods in which the road friction characteristics are inferred from the tyre responses: tyre slip, tyre vibration, and tyre noise. Slip-based approaches (longitudinal dynamics, lateral dynamics, and tyre self-alignment moment) are covered in the first part of the review, while low frequency and high frequency vibration-based works are presented in the following sections. Finally, a brief summary containing the main advantages and drawbacks derived from each estimation method and the future envisaged research lines are presented in the last sections of the paper.

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Steady-state drifting stabilization of RWD vehicles

Cited by 106 publications

References 19 publications

Rear wheel torque vectoring model predictive control with velocity regulation for electric vehicles

Rear wheel torque vectoring model predictive control with velocity regulation for electric vehicles

High-Speed Autonomous Drifting With Deep Reinforcement Learning

Road Friction Virtual Sensing: A Review of Estimation Techniques with Emphasis on Low Excitation Approaches

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