Aeroengine LQR Control Based on Fuzzy-Neural Networks

To solve the problem of Linear Quadratic Regulator (LQR) in aeroengine multivariable control,a modified LQR method based on a kind of fuzzy-neural networks was presented.First,we chose some representative operation points in the engine flight envelope,and designed the LQR controller for each operating point separately.Then,Adaptive-Network-based Fuzzy Inference System (ANFIS) was utilized to synthesize each linear controller to make a nonlinear controller.The inputs of ANFIS are flight altitude and Mach number,and the output is a feedback matrix,which represents the design results.The design of other operation points in the engine flight envelope can be conveniently deduced by the inference system.The ANFIS was trained offline,so we concluded that the method could compensate the short comings of the LQR control.Simulation results were given for a specific turbofan.The design process was analyzed,and the results prove the validity of the method.