Optimal control of brakes and steering for autonomous collision avoidance using modified Hamiltonian algorithm
Journal article, 2019

This paper considers the problem of collision avoidance for road vehicles, operating at the limits of friction. A two-level modelling and control methodology is proposed, with the upper level using a friction-limited particle model for motion planning, and the lower level using a nonlinear 3DOF model for optimal control allocation. Motion planning adopts a two-phase approach: the first phase is to avoid the obstacle, the second is to recover lane keeping with minimal additional lateral deviation. This methodology differs from the more standard approach of path-planning/path-following, as there is no explicit path reference used; the control reference is a target acceleration vector which simultaneously induces changes in direction and speed. The lower level control distributes vehicle targets to the brake and steer actuators via a new and efficient method, the Modified Hamiltonian Algorithm (MHA). MHA balances CG acceleration targets with yaw moment tracking to preserve lateral stability. A nonlinear 7DOF two-track vehicle model confirms the overall validity of this novel methodology for collision avoidance.

stability control

active safety

collision avoidance

vehicle control

Vehicle dynamics


Yangyan Gao

University of Lincoln

Timothy Gordon

University of Lincoln

Mathias R Lidberg

Chalmers, Mechanics and Maritime Sciences, Vehicle Engineering and Autonomous Systems

Vehicle System Dynamics

0042-3114 (ISSN)

Vol. 57 8 1224-1240

Subject Categories

Vehicle Engineering


Control Engineering



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