Robust lap-time simulation

Lap-time simulation is an important computational tool for many motor racing teams, but the assumption of ideal operating conditions and a ‘perfect’ racing driver is usually made. The aim of this paper is to extend lap-time simulation to include robustness to disturbances on the vehicle and robustness to mistakes by the driver. A previously developed receding-horizon model predictive control with a convex optimisation method for lap-time simulation is extended with a tube-based technique for robust model predictive control. A linear quadratic regulator compensatory controller maintains the vehicle close to the nominal vehicle path and speed trajectories in the presence of disturbances to the vehicle. An ensemble of disturbed trajectories defines a tube of trajectories, which is then used to modify the nominal vehicle path so that the track boundary constraints are satisfied in the presence of the disturbances. The lap time versus r.m.s. steering velocity curves for two different vehicle models demonstrate the expected trade-off: a decrease in the activity of the compensatory steering control increases the tube width, which leads to a longer lap time. The technique has application to designing and setting up cars to perform well in the presence of disturbances.

[1]  Robin S. Sharp,et al.  Time-optimal control of the race car: a numerical method to emulate the ideal driver , 2010 .

[2]  David J. Cole,et al.  Efficient minimum manoeuvre time optimisation of an oversteering vehicle at constant forward speed , 2011, Proceedings of the 2011 American Control Conference.

[3]  David Q. Mayne,et al.  Tube‐based robust nonlinear model predictive control , 2011 .

[4]  J. D. Robson ROAD SURFACE DESCRIPTION AND VEHICLE RESPONSE , 1979 .

[5]  David J. Cole,et al.  Minimum Maneuver Time Calculation Using Convex Optimization , 2013 .

[6]  B. Anderson,et al.  Optimal control: linear quadratic methods , 1990 .

[7]  D L Brayshaw,et al.  A quasi steady state approach to race car lap simulation in order to understand the effects of racing line and centre of gravity location , 2005 .

[8]  David Q. Mayne,et al.  Robust model predictive control using tubes , 2004, Autom..

[9]  Eric C. Kerrigan,et al.  Efficient robust optimization for robust control with constraints , 2008, Math. Program..

[10]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..

[11]  David J. Cole,et al.  A path-following driver–vehicle model with neuromuscular dynamics, including measured and simulated responses to a step in steering angle overlay , 2012 .

[12]  J. D. Robson,et al.  The description of road surface roughness , 1973 .

[13]  David J. Cole,et al.  Predictive and linear quadratic methods for potential application to modelling driver steering control , 2006 .

[14]  David J. Cole,et al.  Vehicle trajectory linearisation to enable efficient optimisation of the constant speed racing line , 2012 .

[15]  D. Mayne,et al.  Min-max feedback model predictive control for constrained linear systems , 1998, IEEE Trans. Autom. Control..

[16]  J. Maciejowski,et al.  Feedback min‐max model predictive control using a single linear program: robust stability and the explicit solution , 2004 .

[17]  David Q. Mayne,et al.  Robust model predictive control of constrained linear systems with bounded disturbances , 2005, Autom..