Development of Distributed Networked Control System For Turbine Engine Efficient Operations
Future turbine engines require more efficient consumption of energy, which is hindered by the added weight of the current turbine engine control systems. This thesis work presents development of an advanced control system to replace the Full Authority Digital Engine Control (FADEC) system currently used by turbine engines. The intent was to investigate a solution which reduces energy consumption and increases efficiency of turbine engine operations. The concept of Distributed Networked Control Systems (DNCS) was used to achieve the design intent. Artificial intelligent techniques were used to overcome the challenges inherent in the DNCS. Specifically, an Artificial Neuro Fuzzy Inference System (ANFIS) was used for state estimation within the control system. The full implementation of the developed DNCS consisted of distributed controllers, state estimators, and a simulated network interface. The developed DNCS was integrated and tested on MAPSS turbine engine simulation model. Test results showed that the DNCS design using an ANFIS state estimator improved turbine engine performance even under severe network delay conditions. As a result, the developed control system proves to be a viable alternative to the current engine control system. The significance of the results of the DNCS is the reduction of engine weight leading to a reduction in energy consumption and an increased engine efficiency.
Electrical engineering|Operations research|Artificial intelligence
"Development of Distributed Networked Control System For Turbine Engine Efficient Operations"
ETD Collection for Tennessee State University.