Development of a procedure for the synthesis of a micro gas turbine engine neural controller with account for fuel consumption constraints

Abstract

Neural networks are often used to model dynamic processes in objects and to synthesize their control systems. However, their application in real systems is now rather limited due to insufficient research into the process of creating control systems. The issues of creating neural network control systems for gas turbine engines, taking into account both their nonlinear dynamics and the fuel consumption constraints depending on the engine operating mode, remain practically unexplored. To take into account the fuel consumption constraints, a method was developed for modifying the misalignment between the actual and target RPM values during the training of the neural controller. The resulting neural controller is characterized by implicit fuel consumption constraints and non-linear dynamics of the engine itself. The developed method for modifying the neural network training error allows one to synthesize a nonlinear control system, taking into account the requirements for limitations in the automatic mode.