Fuzzy Computing for Control of Aero Gas Turbine Engines .

Many methods, techniques and procedures available for designing the control system of plants and processes, are applied only after knowing accurately the plant or process to be controlled. However, in some complex situations where plants/processes cannot be accurately modelled, and especially where their control has human interaction, controller design may not be completely satisfactory. In such cases, it has been found that control decisions can be made on the basis of heuristic/linguistic measures or fuzzy algorithms. Fuzzy set principles have been used in controlling various plants/processes ranging from a laboratory steam engine to an autopilot, including an aero gas turbine engine engine for which the response of the engine speed for a fuzzy input of fuel flow has been studied. In this paper, certain stipulations and logic are suggested for the control of the total gas turbine engine. A case study of a single spool aero gas turbine engine with one of its state variables varied by heuristic logic is presented

Design and analysis of a class of fuzzy gain controller.

by Lee Wai Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 118-[124]).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Introduction --- p.1Chapter 1.2 --- Review of Previous Work --- p.3Chapter 1.3 --- Scope of the Thesis --- p.4Chapter 2 --- Background Knowledge of Fuzzy Control System --- p.7Chapter 2.1 --- Introduction --- p.7Chapter 2.2 --- Fuzzy Sets --- p.7Chapter 2.2.1 --- Properties of Fuzzy Sets --- p.10Chapter 2.2.2 --- Operations on Fuzzy Sets --- p.13Chapter 2.3 --- Fuzzy Models --- p.14Chapter 2.3.1 --- Linguistic Model --- p.15Chapter 2.3.2 --- Takagi-Sugeno-Kang (TSK) Fuzzy Model --- p.16Chapter 2.4 --- Fuzzy Inference System --- p.17Chapter 2.4.1 --- Fuzzifier --- p.18Chapter 2.4.2 --- Knowledge Base --- p.19Chapter 2.4.3 --- Inference Engine --- p.19Chapter 2.4.4 --- Defuzzifier --- p.20Chapter 2.4.5 --- Product-Sum-Gravity Inference --- p.21Chapter 3 --- Decomposition of Fuzzy Rules --- p.25Chapter 3.1 --- Introduction --- p.25Chapter 3.2 --- Decomposability of Fuzzy Inference System --- p.26Chapter 3.3 --- The Decomposability condition --- p.29Chapter 3.4 --- Determining Decomposed Parameters --- p.32Chapter 3.5 --- Decomposable Approximation --- p.35Chapter 3.5.1 --- Linear Approximation --- p.38Chapter 3.5.2 --- Case Study --- p.40Chapter 3.6 --- Limitation of Decomposable Approximation --- p.42Chapter 3.7 --- Approximation Index --- p.44Chapter 3.7.1 --- Case Study --- p.48Chapter 3.8 --- Decomposable TSK Model --- p.52Chapter 3.8.1 --- Case Study --- p.54Chapter 3.9 --- Conclusion --- p.56Chapter 4 --- Fuzzy Identification --- p.58Chapter 4.1 --- Introduction --- p.58Chapter 4.2 --- Least-squares Estimation --- p.59Chapter 4.3 --- LSE Formulation of Various Fuzzy Models --- p.63Chapter 4.3.1 --- Linguistic Model --- p.63Chapter 4.3.2 --- TSK Model --- p.69Chapter 4.3.3 --- Decomposable System --- p.75Chapter 4.3.4 --- Comparative Case Study --- p.79Chapter 4.4 --- Fuzzy Regional System Identification --- p.81Chapter 4.4.1 --- Case Study --- p.86Chapter 4.5 --- Recursive Estimation --- p.86Chapter 4.5.1 --- Case Study --- p.90Chapter 4.6 --- Conclusion --- p.90Chapter 5 --- Performance-Based Fuzzy Gain Controller --- p.92Chapter 5.1 --- Introduction --- p.92Chapter 5.2 --- Conventional Fuzzy Control --- p.93Chapter 5.3 --- Fuzzy Gain Control --- p.95Chapter 5.4 --- Design Algorithm --- p.97Chapter 5.5 --- Stability Design Approach --- p.98Chapter 5.6 --- Simulation Case Study --- p.102Chapter 5.7 --- Conclusion --- p.106Chapter 6 --- Identification/Control Design Example --- p.107Chapter 7 --- Conclusion --- p.115Bibliography --- p.11