access icon free Multi-mode switching-based model predictive control approach for longitudinal autonomous driving with acceleration estimation

© The Institution of Engineering and Technology
Received 27/07/2020, Accepted 27/01/2021, Revised 16/01/2021, Published 01/02/2021

This study proposes a multi-mode switching longitudinal autonomous driving system based on model predictive control (MPC) with acceleration estimation of proceeding vehicle. A hierarchical control framework composed of three layers is utilised. In the first layer, five longitudinal driving scenarios are defined based on emergency degree. In the second layer, the MPC for longitudinal autonomous driving is designed and serving as the upper controller. Among which a non-linear tracking differentiator is used for acceleration estimation of preceding vehicle. In the third layer, the inverse longitudinal vehicle system dynamic model with strong non-linearity is considered in the lower controller. Proportional–integral–derivative feedback and feedforward control are combined to track the desired acceleration. Simulation and hardware-in-loop test results show that the multi-mode switching longitudinal autonomous driving system is feasible and effective, and has important value for engineering application.

Inspec keywords: feedforward; predictive control; nonlinear control systems; feedback; driver information systems; three-term control; control system synthesis

Other keywords: upper controller; desired acceleration; lower controller; inverse longitudinal vehicle system dynamic model; feedforward control; longitudinal driving scenarios; proceeding vehicle; MPC; nonlinear tracking differentiator; acceleration estimation; hierarchical control framework; multimode switching-based model predictive control approach; longitudinal autonomous driving system

Subjects: Control system analysis and synthesis methods; Optimal control; Traffic engineering computing; Nonlinear control systems

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