Model predictive control on wall wetting effect using Markov Chain Monte Carlo

Wall wetting is a typical phenomenon in Port Fuel Injection (PFI) engines. The injected fuel is atomized into tiny droplets so as to form a mixture of the vaporized fuel and air. Certain portion of the vaporized fuel or fuel droplet would condense on the wall of the intake manifold and intake value, forming the fuel puddle, which has a significant impact on the air to fuel ratio (A/F ratio) of the cylinder. Detrimental A/F ratio excursion could occur during engine transient operations, giving rise to excessive exhaust emissions and extra fuel consumption, as well as the driveability and catalyst efficiency problems. Hence, some advanced control schemes should be applied. The model predictive control methodology is proposed to compensate for A/F ratio excursion during engine transient operations at throttle tip in or tip out. In light of the fact that additional computational complexity involves in nonlinear intelligent control approaches and big approximation error occurs in simple linear approaches, the relatively simple but powerful nonlinear Markov Chain Monte Carlo (MCMC) is introduced to solve this problem. MCMC is a general approach for obtaining random samples at the stationary probability to substitute arbitrary type of the posterior density. The discrete-time Markov chain is introduced whose state reaches the desired distribution after large numbers of iterations. It is combined with Monte Carlo integration to implement numerical integration. Excellent match between the model prediction data and actual experimental data is observed using the MCMC approach with the low computation cost.