Enhanced PID Controller Using Modified Neural Networks with Pseudo-Linear Connections

This research is a research continuation in the PID controller development and modernization field based on neural networks. Through the pseudo-linear component with amplitude suppression and another with phase advance incorporation, embedded within the nonlinear PID controller structure, previously employed effectively as a mechanism for regulating the helicopter turboshaft engines free turbine rotor speed with a linear electronic regulator, has undergone modernization. The nonlinear PID controller is implemented as a dynamic neural network that directly transmits data, comprising neurons with a radial basis activation function in the initial layer and adalines neurons with a linear activation function in the subsequent layer. In alignment with the aforementioned, this neural network undergoes modification. The pseudo-linear link with amplitude suppression is integrated as an additional layer comprising linear neurons with a sigmoid activation function. Meanwhile, the pseudo-linear link with phase advance is integrated as a parallel layer featuring linear neurons with a sigmoid activation function. Test research findings demonstrate accuracy levels of up to 0.998 in addressing the intricate dynamic systems (demonstrated by helicopter turboshaft engines free turbine rotor speed) controlling parameters task.