Neural Network Modeling of a Tuned PID Controller
In this paper, a Neuro-PID controller model has been developed to improve on the response and performance of a conventional Proportionate Integral Derivative (PID) controller in a nonlinear dynamic environment by developing a self-tuning/adaptive Neural-PID controller. The proposed Neuro-controller was developed using the back propagation algorithm. The gradient descent method was employed for the learning rate, to obtain the initial weight and used in each iteration, in order to accelerate the speed of convergence. Runge-kutta's algorithm was employed to predict the system behavior over time for subsequent weight adaptation.