De Villiers, Johan PieterGodsill, S.J.Singh, S.S.2012-10-052012-10-052011-05J.P. de Villiers, S.J. Godsill & S.S. Singh, Particle predictive control, Journal of Statistical Planning and Inference, vol. 141, no. 5, pp. 1753-1763 (2011), doi: 10.1016/j.jspi.2010.11.025.0378-3758 (print)1873-1171 (online)10.1016/j.jspi.2010.11.025http://hdl.handle.net/2263/20051This work explores the use of sequential and batch Monte Carlo techniques to solve the nonlinear model predictive control (NMPC) problem with stochastic system dynamics and noisy state observations. This is done by treating the state inference and control optimisation problems jointly as a single artificial inference problem on an augmented state-control space. The methodology is demonstrated on the benchmark car-up-the-hill problem as well as an advanced F-16 aircraft terrain following problem.en© 2010 Elsevier. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Journal of Statistical Planning and Inference. 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. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Statistical Planning and Inference, vol 141, issue 5, May 2011, doi:10.1016/j.jspi.2010.11.025.Stochastic controlParticle filterSAME algorithmModel predictive controlMoving horizon controlMarkov chain Monte Carlo (MCMC)Predictive controlMonte Carlo methodMarkov processesPostprint Article