Adaptive Neuro-PID Controller Design with Application to Nonlinear Water Level in NEKA Power Plant

In this study, two novel adaptive PID-like controllers capable of controlling multi-variable, non-linear Multi-Input Multiple-Output (MIMO) systems are proposed. The proposed controllers are designed based on neural networks techniques. The learning algorithms are derived according to minimization of the error between the output of the system and the desired output. At first, two kinds of PID-like neural network controller named neural network PID and Neural network PID with internal dynamic feedbacks are introduced. Both of the proposed controllers may be used for controlling multivariable systems. The difference between these two controllers is mainly in the structure of their hidden layers that leads to their different performance. These controllers are applied to different kinds of black box, linear or nonlinear and time variant or time invariant systems. The stability of the proposed algorithm is also proven mathematically. Compared to conventional methods, very good results are achieved using the proposed methods. The simulation results show the quality performance of the proposed adaptive controllers and algorithms. Finally to show the performance of the proposed method, it is applied to the water level of tanks in water refinement process in NEKA Power Plant which is generally a very nonlinear system. Simulation results in this study show good performance of the proposed adaptive controllers.