Modified Hamiltonian Algorithm for Optimal Lane Change With Application to Collision Avoidance

Abstract

Abstract-This paper deals with collision avoidance for road vehicles when operating at the limits of available friction. For collision avoidance, a typical control approach is to: (a) define a reference geometric path that avoids collision; (b) apply low-level control to perform path following. However, there are a number of limitations in this approach, which are addressed in the current paper. First, it is typically unknown whether a predefined reference path is feasible or over-conservative. Secondly, the control scheme is not well suited to avoiding a moving object, e.g. another vehicle. Further: incorrect choice of reference path may degrade performance, fast adaptation to friction change is not easy to implement and the associated low-level control allocation may be computationally intensive. In this paper we use the general nonlinear optimal control formulation, include some simplifying assumptions and base optimal control on the minimization of an underlying Hamiltonian function. A particle model is used to define an initial reference in the form of a desired global mass-center acceleration vector. Beyond that, yaw moment is taken into account for the purpose of enhancing the stability of the vehicle. The Hamiltonian function is adapted as a linear function of tyre forces and can be minimized locally for individual wheels; this significantly reduces computational workload compared to the conventional approach of forcemoment allocation. Several combinations of actuators are studied to show the general applicability of the control algorithm based on a linear Hamiltonian function. The method has the potential to be used in future vehicle control systems across a wide range of safety applications and hence improve overall vehicle agility and improve safety.

Optimal motion control for collision avoidance at Left Turn Across Path/Opposite Direction intersection scenarios using electric propulsion

“…The Modified Hamiltonian Algorithm (MHA) Controller was first formulated and presented in [30,31] and was in turn inspired by the work in [32]. The controller is motivated by optimal control theory wherein the minimization of the objective function can be reduced to a minimization of the Hamiltonian.…”

Optimal control of brakes and steering for autonomous collision avoidance using modified Hamiltonian algorithm

“…Tyre forces (F t xi , F t yi ) are modelled via a standard Pacejka magic tyre (MF) formula, see Equation 13, which is a load-dependent combined-slip model, using normalized slip and isotropic similarity scaling [15,16]. Further details of the MF model are given in [10,17].…”

“…For yaw moment control, λ is adapted to track a desired yaw moment M d z . In previous work [10][11][12] a switched adaptation rule was used…”

“…Previously, a novel control method termed the Modified Hamiltonian Algorithm (MHA) [10][11][12] was developed for an Autonomous Emergency Cornering system to prevent highway vehicles from dangerous lane departure due to excessive speed. The underlying control concept has been tested with an experimental vehicle and the results suggest that MHA can run efficiently in real time with a highly satisfactory control performance [11].…”

Unified decision-making and control for highway collision avoidance using active front steer and individual wheel torque control

“…By contrast, in [20], an approach called Modified Hamiltonian Algorithm (MHA) is proposed for direct torque distribution. In this approach, first a global acceleration vector is determined using a particle model; secondly, vehicle stability control is considered by constraining yaw moment to follow a desired yaw moment profile; lastly, control allocation is achieved by minimizing a Hamiltonian function at individual wheel level.…”