State-Space Description of the Marine Plant

Abstract

As already mentioned, the first step towards the application of advanced control schemes, ensuring both disturbance rejection and robustness against the various forms of uncertainty, is the construction of a state-space model for the operation of the plant. This chapter is concerned with the development of a non-linear state-space model that can depict the marine engine¡ªturbocharger dynamical interaction and operation, on the one hand, and integrate the inherent physical uncertainty and disturbance on the other hand. The state-space model is derived using the nonlinear mapping abilities of artificial neural nets. Although neural nets have been employed in the past for the description of engine physicochemical processes, especially in the automotive industry, the approach presented here is different for two major reasons. First, the approach presented does not aim to fill some “gap of understanding” in an otherwise full physical modelling picture. Quite the opposite, it employs the full picture of the cycle-mean, quasi-steady, thermodynamic model of Chapter 2 in order to bypass it, because it requires the numerical solution of a non-linear, perplexed algebraic system of equations. Moreover, the neural nets are treated rather as “mathematical objects” than as part of a global approach to intelligent powerplant modelling and control. Therefore, the mathematical expressions corresponding to the typical feedforward neural net structure with one hidden layer are manipulated analytically in order to derive a linearised, yet uncertain, perturbation state-space model.