Abstract

This paper is concerned with the controller design problem for a class of networked main stream temperature control system with long random time delay and packet losses. To compensate the effect of time delay and packet losses, a gain-scheduling based Smith-PID controller is proposed for the considered networked control systems (NCSs). Moreover, to further improve the control performance of NCSs, genetic algorithm is employed to obtain the optimal control parameters for gain-scheduling Smith-PID controller. Simulation results are given to demonstrate the effectiveness of the proposed methods.

Highlights

  • In recent years, Networked control systems (NCSs) have received increasing attentions

  • Because of the insertion of network, NCSs inevitably have the problems of random time delay, packet outof-order and packet losses, etc

  • From the simulation results we can see that the proposed gain-scheduling based Smith-PID network controller shows better control performance than normal Smith-PID controller, which demonstrates the effectiveness of the proposed method

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Summary

INTRODUCTION

In recent years, Networked control systems (NCSs) have received increasing attentions. In [5] and [6], the problem of designing H∞ controllers for networked control systems with both network-induced time delay and packet out-of-order was studied. In [8], the problem of designing guaranteed cost controller is studied for a class of uncertain time delay networked control system. Aiming at the problem of random time delay of the networked control systems, the gain-scheduling based SmithPID controller is designed in this paper. The Smith controller is used to compensate the long time delay of the system, and the gain-scheduling based PID scheduling controller is used to solve the problem of random time delay of the networked control systems. The Smith control design and the gain-scheduling networked control design are introduced respectively.

PROBLEM FORMULATION
Smith-PID Control Design
Scheduling Network Control Design
SIMULATION
CONCLUSIONS
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