A New PID Neural Network Controller Design for Nonlinear Processes

TL;DR

This paper introduces a novel adaptive PID neural network controller for nonlinear processes, employing an improved gradient descent method to enhance stability, robustness, and tracking performance under disturbances.

Contribution

It presents a new adaptive tuning approach for PIDNN controllers using an improved gradient descent and stability margin for nonlinear processes.

Findings

Enhanced tracking performance demonstrated in simulations

Improved robustness against load disturbances

Superior results compared to existing learning methods

Abstract

In this paper, a novel adaptive tuning method of PID neural network (PIDNN) controller for nonlinear process is proposed. The method utilizes an improved gradient descent method to adjust PIDNN parameters where the margin stability will be employed to get high tracking performance and robustness with regard to external load disturbance and parameter variation. Simulation results show the effectiveness of the proposed algorithm compared with other well-known learning methods.