Weight Initialization on Neural Network for Neuro PID Controller: Case study

Neuro PID controller has been widely used in control field in recent times. Random weight initialization is used in the Neuro PID controller. The impact of various weight initialization has not been studied in the Neuro PID controller. The weight initialization methods such as Xavier initialization and He initialization have been proven to be effective in faster convergence in neural network. This paper investigated a weight initialization concept in Neuro PID controller by case studying with zero initialization, constant initialization, Gaussian distributed initialization, uniform distributed initialization, He initialization, and Xavier initialization in typical first-order lag elements, integrator elements, and dead time elements to obtain suitable initialization of weight coefficients, which reduces settling time for the neural network.