Wael Khatib

The Stud GA: A Mini Revolution?

This paper presents a new approach to function optimisation using a new variant of GAs. This algorithm is called the Stud GA. Instead of stochastic selection, the fittest individual, the Stud, shares its genetic information with all others using simple GA operators. The standard Gray coding is maintained. Simple techniques are added to maintain diversity of the population and help achieve the global optima in difficult multimodal search spaces. The benefits of this approach are an improved performance in terms of accuracy, efficiency and reliability. This approach appears to be able to deal with a wide array of functions and to give consistent repeatability of optimisation performance. A variety of test functions is used to illustrate this approach. Results presented suggest a viable and attractive addition to the portfolio of evolutionary computing techniques.

Performance optimization of gas turbine engine

Performance optimization of a gas turbine engine can be expressed in terms of minimizing fuel consumption while maintaining nominal thrust output, maximizing thrust for the same fuel consumption and minimizing turbine blade temperature. Additional control layers are used to improve engine performance. This paper presents an evolutionary approach called the StudGA as the optimization framework to design for optimal performance in terms of the three criteria above. This approach converges fast and can potentially save on computing cost. Model-based experimental results are used to illustrate this approach.

Control system design for a gas turbine engine using evolutionary computing for multidisciplinary optimization

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.

Nonlinear control system design using variable complexity modelling and multiobjective optimization

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.

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/sub /spl infin// 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/sub /spl infin// 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.

Multidisciplinary optimization with evolutionary computing for control design

Abstract Multidisciplinary optimization (MDO) is concerned with complex systems exhibiting challenges in terms of organization and scale. Complex multivariable control design can benefit from advances in MDO architectures. An example of model-based PI control design of a gas turbine engine is presented Three MDO architectures including collaborative optimization are applied to this example. The optimization is earned out through a multiobjective genetic algorithm framework. Implementation and results provided suggest viability of such techniques for complex control design problems and potential improvements over existing techniques.

Performance optimization of gas turbine engines using the studga

Abstract Control of gas turbine engines is concerned with producing adequate thrust while maintaining stable and safe operation. Additional control layers can allow improvements in engine performance. Performance optimization can be expressed in terms of: minimizing fuel consumption while maintaining nominal thrust output, maximizing thrust for the same fuel consumption and minimizing turbine blade temperature. A new evolutionary approach called the StudGA is used as the optimization framework to design for optimal performance in terms of the three categories above. This approach gives faster convergence and can potentially save on expensive simulations. Model-based experimental results are used to illustrate these approache