Design synergy through variable complexity architectures

This paper presents a multi-stage design approach that uses a multiobjective genetic algorithm (MOGA) as the framework for optimization and multiobjective preference articulation. An H_∞ loop-shaping technique is used to design controllers based on a linear state-space model of a gas turbine engine (GTE). A non-linear model is then used to assess performance of the controller in meeting various stability, design and performance requirements. The computational load of applying MOGA to the design of a control system for the Spey engine for the H_∞ strategy is very high. Alternative model approximations, response surface models, are used in order to speed up the design process. Regression analysis is applied to fit linear models to this data for various control responses. To assist the design process, a neural network is trained to classify possible designs to avoid unstable solutions. These simple models are used to design the controller within the framework of a MOGA. The final designs are checked using the original non-linear model. Good results indicate the viability of this approach for application to complex designs involving expensive computational models