Advanced controller design for aircraft gas turbine engines

Abstract

In this paper, NARMAX and neural network representations are shown to be suitable for modelling a gas turbine engine. These models then provide the basis for the model-based control strategies. Due to the nonlinearities of the engine and constraints on the fuel feed, a proportional, integral and derivative (PID) controller cannot cope with its whole operating range and therefore a gain-scheduling PID controller is required. Since the parameters in the gain-scheduling PID controller need to be changed with the operating range, there is a need for a global nonlinear controller. Model predictive control is then proposed as a solution to this problem. Two methods are considered, one is approximate model predictive control (AMPC) using the instantaneous linearisation of a nonlinear model incorporating the generalised predictive control and the other is the nonlinear model predictive control (NMPC). Their properties are presented and discussed. Both provide good control performance for the engine across its whole operating range. Since AMPC can be obtained analytically, requires less computation time and avoids the local minimum, it also provides the better control performance in the face of disturbance and model uncertainties. So it is preferred to NMPC. The results illustrate the improvements in control performance that can be achieved to that of gain-scheduling PID controllers.