Toward human-machine coordination: A hierarchical control approach for semi-autonomous drifting

Abstract

The advancement in modern control technology makes it possible for intelligent vehicles to go beyond the traditional controllable region and expand the ability to cope with extreme conditions, such as drift with large sideslips. This paper proposes a hierarchical shared drifting control (H-SDC) approach for automated vehicles, aiming to assist inexperienced drivers in completing drift maneuvers, while leaving sufficient control freedom for them. For the cooperative decision layer, the driver intention is integrated into the reference drift equilibrium calculation via two fuzzy inference systems in longitudinal and lateral directions. The operating mode of the collaborative system together with the switching strategy is meticulously defined subsequently. For the real-time execution layer, a robust controller based on time-triggered linear parametric varying-model predictive control (LPV-MPC) is developed for the dynamic drifting state tracking. Efficient implementation of the control algorithm is achieved with the LPV model compensation. Stability limits are specified by an extended stability-oriented safety envelope (SOSE) in the phase plane. The proposed H-SDC framework is verified with both numerical simulation and human-in-the-loop experiment. The results illustrate the effectiveness and superiority of the semi-autonomous system in terms of stability control and drift facilitation. The subjective questionnaire survey further indicates its accessible advantages for novice drivers to learn drifting skills and enjoy the driving pleasure.

Keywords

drift control shared driving vehicle dynamics nonlinear model predictive control limit handling

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