Title :
Fuzzy scheduling control of a gas turbine aero-engine: a multiobjective approach
Author :
Chipperfield, Andrew J. ; Bica, Beatrice ; Fleming, Peter J.
Author_Institution :
Dept. of Autom. Control & Syst. Eng., Univ. of Sheffield, UK
fDate :
6/1/2002 12:00:00 AM
Abstract :
This paper investigates the use of a nonconventional approach to control a gas turbine aero-engine. The rationale behind this study is the need to develop advanced tools and techniques that can assist in improving the performance of the system and simultaneously enhance the flexibility of the control strategy. Modern techniques are required for many complex systems where increasingly strict performance and regulatory requirements must be achieved. This is particularly true of aerospace systems where consideration of safety, reliability, maintainability, and environmental impact are all necessary as part of the control requirements. This paper investigates a combination of two such potential techniques: fuzzy logic and evolutionary algorithms. Emerging from new requirements for gas turbine aero-engine control, a flexible gain scheduler is developed and analyzed. A hierarchical multiobjective genetic algorithm is employed to search and optimize the potential solutions for a wide envelope controller covering idle, cruise, and full-power conditions. The overall strategy is demonstrated to be a straightforward and feasible method of refining the control system performance and increasing its flexibility
Keywords :
control system synthesis; fuzzy control; gas turbine power stations; gas turbines; genetic algorithms; optimal control; power station control; control design; decision making; environmental impact; evolutionary algorithms; fuzzy logic; fuzzy scheduling; gas turbine aero-engine; hierarchical multiobjective genetic algorithm; maintainability; multiobjective fuzzy scheduling control; performance requirements; regulatory requirements; reliability; safety; Aerospace control; Aerospace safety; Control systems; Evolutionary computation; Fuzzy control; Fuzzy logic; Genetic algorithms; Maintenance; System performance; Turbines;
Journal_Title :
Industrial Electronics, IEEE Transactions on
DOI :
10.1109/TIE.2002.1005378