Soft computing in engineering design optimisation

The implementation of Soft Computing methodologies in two aerospace design problems is presented, one being the design of quiet and efficient aircraft propellers, and the other being the manoeuvre control of a satellite. They were chosen as they present very challenging engineering design problems with nonlinearities and discontinuities in the design space. The methodologies used include Simulated Annealing for design optimisation, Neural Networks for system representation and Fuzzy Logic for system control. The choice of these methods over conventional analytical techniques is shown to enable the solution of these design problems. The propeller design methodology described produces designs that have equivalent or improved performance and significantly reduced noise when compared to commercial off-the-shelf designs. The optimal satellite controller described shows significant improvements in reduced settling time and overshoot when compared to conventional controllers.

[1]  Anthony V. Fiacco,et al.  Nonlinear programming;: Sequential unconstrained minimization techniques , 1968 .

[2]  Ramon Parra-Loera,et al.  Optimal self-tuning fuzzy controller , 1995, Other Conferences.

[3]  D. S. Weir,et al.  Aircraft Noise Prediction Program theoretical manual: Propeller aerodynamics and noise , 1986 .

[4]  Alan L. Yuille,et al.  Winner-take-all mechanisms , 1998 .

[5]  P. J. Gage,et al.  New approaches to optimization in aerospace conceptual design , 1995 .

[6]  Nikhil R. Pal,et al.  Learning fuzzy rules for controllers with genetic algorithms , 2003, Int. J. Intell. Syst..

[7]  L. Ingber.  Very fast simulated re-annealing , 1989 .

[8]  Roger M. Y. Ho,et al.  Goal programming and extensions , 1976 .

[9]  Dan M. Somers,et al.  A computer program for the design and analysis of low-speed airfoils, supplement , 1980 .

[10]  Martin A. Riedmiller,et al.  Advanced supervised learning in multi-layer perceptrons — From backpropagation to adaptive learning algorithms , 1994 .

[11]  Bruce E. Rosen,et al.  Genetic Algorithms and Very Fast Simulated Reannealing: A comparison , 1992 .

[12]  Jasbir S. Arora,et al.  Introduction to Optimum Design , 1988 .

[13]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[14]  W. E. Thompson,et al.  A design methodology for fuzzy controllers and comparison with an optimal PD controller for a nonlinear control system , 1993, Proceedings of 36th Midwest Symposium on Circuits and Systems.

[15]  Greg L. Zacharias,et al.  Hybrid fuzzy logic flight controller synthesis via pilot modeling , 1995 .

[16]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[17]  Kwang Y. Lee,et al.  Two approaches for automating the tuning process of fuzzy logic controllers [PWR application] , 1993, Proceedings of 32nd IEEE Conference on Decision and Control.

[18]  F. Farassat.  Linear Acoustic Formulas for Calculation of Rotating Blade Noise , 1981 .

[19]  E. Eugene Larrabee,et al.  Practical Design of Minimum Induced Loss Propellers , 1979 .

[20]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[21]  Wei Li.  Optimization of a fuzzy controller using neural network , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[22]  Abdollah Homaifar,et al.  Design using genetic algorithms of hierarchical hybrid fuzzy‐PID controllers of two‐link robotic arms , 1997 .