Trajectory tracking for autonomous vehicles on varying road surfaces by friction-adaptive nonlinear model predictive control

References

• Rawlings J, Mayne D. Model predictive control: theory and design. Madison, WI; Nob Hill Publishing, LLC; 2002.
   Google Scholar
• Hrovat D, Di Cairano S, Tseng H. The development of model predictive control in automotive industry: a survey. IEEE Conference on Control Applications; Dubrovnik, Croatia; 2012. p. 295–302.
   Google Scholar
• Di Cairano S, Kolmanovsky IV. Real-time optimization and model predictive control for aerospace and automotive applications. American Control Conference; Milwaukee, WI; 2018. p. 2392–2409.
   Google Scholar
• Falcone P, Borrelli F, Asgari J, et al. Predictive active steering control for autonomous vehicle systems. IEEE Trans Control Sys Technol. 2007;15(3):566–580. doi: 10.1109/TCST.2007.894653
   Web of Science ®Google Scholar
• Di Cairano S, Tseng H, Bernardini D, et al. Vehicle yaw stability control by coordinated active front steering and differential braking in the tire sideslip angles domain. IEEE Trans Control Sys Technol. 2013;21(4):1236–1248. doi: 10.1109/TCST.2012.2198886
   Web of Science ®Google Scholar
• Di Cairano S, Kalabić U, Berntorp K. Vehicle tracking control on piecewise-clothoidal trajectories by MPC with guaranteed error bounds. IEEE Conference on Decision and Control; Las Vegas, NV; 2016.
   Google Scholar
• Quirynen R, Berntorp K, Di Cairano S. Embedded optimization algorithms for steering in autonomous vehicles based on nonlinear model predictive control. American Control Conference; Milwaukee, WI; 2018.
   Google Scholar
• Gustafsson F. Slip-based tire-road friction estimation. Automatica. 1997;33:1087–1099. doi: 10.1016/S0005-1098(97)00003-4
   Web of Science ®Google Scholar
• Braghin F, Cheli F, Sabbioni E. Environmental effects on Pacejka's scaling factors. Vehicle Syst Dyn. 2006 Jul;44(7):547–568. doi: 10.1080/00423110500520065
   Web of Science ®Google Scholar
• Lundquist C, Schön TB. Recursive identification of cornering stiffness parameters for an enhanced single track model. 15th IFAC Symposium on System Identification; Saint-Malo, France; 2009.
   Google Scholar
• Berntorp K, Di Cairano S. Tire-stiffness and vehicle-state estimation based on noise-adaptive particle filtering. IEEE Trans Control Syst Technol. 2018;27(3):1100–1114. doi: 10.1109/TCST.2018.2790397
   Web of Science ®Google Scholar
• Quirynen R, Knyazev A, Di Cairano S. Block structured preconditioning within active-set based real-time optimal control. European Control Conference; Limassol, Cyprus; 2018.
   Google Scholar
• Berntorp K, Olofsson B, Lundahl K, et al. Models and methodology for optimal trajectory generation in safety-critical road–vehicle manoeuvres. Vehicle Syst Dyn. 2014;52(10):1304–1332. doi: 10.1080/00423114.2014.939094
   Web of Science ®Google Scholar
• Pacejka HB. Tire and vehicle dynamics. 2nd ed. Oxford, UK: Butterworth-Heinemann; 2006.
   Google Scholar
• Berntorp K. Particle filtering and optimal control for vehicles and robots [dissertation]. Department of Automatic Control, Lund University, Sweden; 2014.
   Google Scholar
• Di Cairano S, Doering J, Kolmanovsky I, et al. Model predictive control of engine speed during vehicle deceleration. IEEE Trans Control Sys Technol. 2014;22(6):2205–2217. doi: 10.1109/TCST.2014.2309671
   Web of Science ®Google Scholar
• Faulwasser T, Findeisen R. Nonlinear model predictive control for constrained output path following. IEEE Trans Automat Control. 2015;61(4):1026–1039. doi: 10.1109/TAC.2015.2466911
   Web of Science ®Google Scholar
• Fletcher R. Practical methods of optimization. Chirchester: John Wiley & Sons; 2013.
   Google Scholar
• Grüne L. NMPC without terminal constraints. IFAC Conference on Nonlinear MPC; Noordwijkerhout, The Netherlands; 2012.
   Google Scholar
• Bock HG, Plitt KJ. A multiple shooting algorithm for direct solution of optimal control problems. IFAC World Congress; Budapest, Hungary; 1984.
   Google Scholar
• Quirynen R, Vukov M, Zanon M, et al. Autogenerating microsecond solvers for nonlinear MPC: a tutorial using ACADO integrators. Optimal Control Appl Methods. 2014;36:685–704. doi: 10.1002/oca.2152
   Web of Science ®Google Scholar
• Diehl M, Findeisen R, Allgöwer F, et al. Nominal stability of the real-time iteration scheme for nonlinear model predictive control. IEE Control Theory Appl. 2005;152(3):296–308. doi: 10.1049/ip-cta:20040008
   Web of Science ®Google Scholar
• Berntorp K, Hoang T, Quirynen R. Autonomous vehicle control design, implementation, and verification using scaled road vehicles. IEEE Conf. on Control Technology and Applications; Copenhagen, Denmark; 2018.
   Google Scholar
• Quirynen R, Knyazev A, Di Cairano S. Projected preconditioning within a block-sparse active-set method for MPC. IFAC Conference on Nonlinear MPC; Madison, WI; 2018.
   Google Scholar
• Gustafsson F. Statistical sensor fusion. Lund, Sweden: Studentlitteratur; 2010.
   Google Scholar
• Frasch JV, Gray A, Zanon M. An auto-generated nonlinear MPC algorithm for real-time obstacle avoidance of ground vehicles. European Control Conference; Zurich, Switzerland; 2013.
   Google Scholar
• Berntorp K. Derivation of a six degrees-of-freedom ground-vehicle model for automotive applications. Dept. Automatic Control, Lund University; 2013. ( 7627).
   Google Scholar