Particle predictive control

Abstract

This 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.