Variable complexity modelling for evolutionary gas turbine control design

Control systems design of complex nonlinear system often involves the use of expensive computational models. To speed up the design process and to allow more designs to be evaluated, an inexpensive approach using variable complexity modelling is introduced. A nonlinear thermodynamic model of a gas turbine engine is used to evaluate a selection of designs for a multivariable PI controller configuration. Regression analysis is applied to fit polynomial models to this data for various control responses. These simple models are used to design the controller within the framework of a multiobjective genetic algorithm. The final designs are checked using the original nonlinear model. Good results indicate the viability of this approach for application to complex designs involving expensive computational models