Olivier, Laurentz EugeneCraig, Ian Keith2017-03-242016-07Olivier, LE & Craig, IK 2016, 'Fault-tolerant nonlinear MPC using particle filtering', IFAC-PapersOnLine, vol. 49, no. 7, pp. 177-182.1474-667010.1016/j.ifacol.2016.07.242http://hdl.handle.net/2263/59523A fault-tolerant nonlinear model predictive controller (FT-NMPC) is presented in this paper. State estimates, required by the NMPC, are generated with the use of a particle filter. Faults are identiced with the nonlinear generalized likelihood ratio method (NL-GLR), for which a bank of particle filters is used to generate the required fault innovations and covariance matrices. A simulated grinding mill circuit serves as the platform for illustrating the use of this fault detection and isolation (FDI) scheme along with the NMPC. The results indicate that faults can be correctly identiced and compensated for in the NMPC framework to achieve optimal performance in the presence of faults.en© 2016 IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in IFAC papers online. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published in IFAC papers online, vol. 49, no. 7, pp. 177-182, 2016. doi : 10.1016/j.ifacol.2016.07.242.Generalized likelihood ratioParticle filterFault-tolerant nonlinear model predictive controller (FT-NMPC)Nonlinear model predictive controller (NMPC)Fault detection and isolation (FDI)Model predictive control (MPC)Postprint Article