Multidisciplinary optimization (MDO) is concerned with complex systems exhibiting challenges in terms of organization and scale. Thus, it is well suited to be applied to complex multivariable control design. Collaborative optimization is one approach for dealing with complex multidisciplinary optimization problems. Three MDO architectures, including collaborative optimization, are applied to control system design for a gas turbine engine, in order to improve the design search process by exploring possible solutions with parallel, but independent search strands. The optimization is carried out through a multiobjective genetic algorithm framework.

To design controllers for complex non-linear systems usually involves the use of expensive computational models. A non-linear thermodynamic model of a gas turbine engine is used to evaluate a selection of designs for a multivariable PI controller configuration. An approach using variable complexity modelling (VCM) is introduced to allow more designs to be evaluated and also to speed up the design process. Response surface methodology (RSM) is a statistical technique in which smooth functions are used to model an objective function. RSM employs statistical methods to create functions, typically polynomials, to model the response or outcome of a numerical experiment in terms of several independent variables. Regression analysis is applied to fit polynomial models to this data for various control responses. These control responses models are evaluated by a multiobjective genetic algorithm to design the controller parameters. The final designs are checked using the original non-linear model.