A Comparison Between Nonlinear and Linear Model Predictive Control for Trajectory-Following and Off-Tracking Minimization of Long Combination Vehicles
Preprint, 2020

In vehicle motion planning and trajectory-tracking control with the objective of driving automation, the vehicle models are commonly linearized. The linearization is either done for specific operating conditions which limits the generality of the designed function; or for the current operating condition, that requires on-line linearization. The latter is commonly used in model predictive control (MPC) and is only valid for a short prediction horizon, as the accuracy of the linear model diminishes moving further from the linearization point. The accuracy of the linear prediction model can be increased if the linearization is performed for all the discrete steps of an initial guess reference trajectory, resulting in a linear time-varying MPC (LTV-MPC). In this paper, we compared the linear and nonlinear models of a long combination vehicle (LCV). We designed a nonlinear MPC (NMPC) for trajectory-following and off-tracking minimization of the LCV. The used model allowed coupled longitudinal and lateral dynamics together with the possibility of a combined steering, propulsion and braking control. We showed that the control actions calculated by the LTV-MPC are relatively close to the ones obtained by the NMPC, if the guess linearization trajectory is sufficiently close to the nonlinear solution. LTV-MPC allows off-line model linearization that is beneficial for real-time implantation of MPC comprising long prediction horizons. The long prediction horizons are needed for motion planning and trajectory-following of LCVs to maintain stability and tracking quality, e.g., by reducing the speed before reaching a curve, and by generating control actions within the actuators limits.

off-tracking minimization

linear-time varying optimal control

nonlinear model predictive control

trajectory-tracking

long combination vehicles

Author

Toheed Ghandriz

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

Bengt J H Jacobson

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

Peter Nilsson

Volvo Autonomous Solutions

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

Leo Laine

Chalmers, Mechanics and Maritime Sciences

Volvo Group

Optimal Distributed Propulsion

VINNOVA, 2015-10-01 -- 2019-12-31.

Swedish Energy Agency, 2015-10-01 -- 2019-12-31.

Driving Forces

Sustainable development

Innovation and entrepreneurship

Areas of Advance

Transport

Energy

Subject Categories

Computational Mathematics

Vehicle Engineering

Control Engineering

Roots

Basic sciences

Learning and teaching

Pedagogical work

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Created

9/1/2020 1