Neural PID Control Strategy for Networked Process Control

Jianhua Zhang,Junghui Chen

doi:10.1155/2013/752489

Abstract

A new method with a two-layer hierarchy is presented based on a neural proportional-integral-derivative (PID) iterative learning method over the communication network for the closed-loop automatic tuning of a PID controller. It can enhance the performance of the well-known simple PID feedback control loop in the local field when real networked process control applied to systems with uncertain factors, such as external disturbance or randomly delayed measurements. The proposed PID iterative learning method is implemented by backpropagation neural networks whose weights are updated via minimizing tracking error entropy of closed-loop systems. The convergence in the mean square sense is analysed for closed-loop networked control systems. To demonstrate the potential applications of the proposed strategies, a pressure-tank experiment is provided to show the usefulness and effectiveness of the proposed design method in network process control systems.

Highlights

Networked control systems (NCSs) make it convenient to control large distributed systems
Process control can integrate the controlled process and the communication network of computational devices, but sensors and actuators cannot be directly used in a conventional way because there are some inherent issues in NCS, such as delay, packet loss, quantization, and synchronization
NCSs can provide Web clients a platform for remote monitoring of the current behavior of the operation plants and for remote control of the plant. These advantages make NCSs applicable to many fields, including spacecraft automotive, remote robots, and manufacturing processes, but not much work has been done in the process control field so far

Summary

Introduction

Networked control systems (NCSs) make it convenient to control large distributed systems. Process control can integrate the controlled process and the communication network of computational devices, but sensors and actuators cannot be directly used in a conventional way because there are some inherent issues in NCS, such as delay, packet loss, quantization, and synchronization. Some efforts have been made to deal with these issues about NCSs. Zhang et al (2012) investigated the stability problem of a class of delayed neural networks with stabilizing or destabilizing time-varying impulses [1]. Tian et al (2008) investigated the observer-based output feedback control algorithm for networked control systems with two quantizers using a set of nonlinear matrix inequalities [3]. A robust and reliable H∞ filter was designed for a class of nonlinear networked control systems with random sensor failure [4]

Objectives

Methods

Conclusion