Research Papers

Decentralized PID Controllers Based on Probabilistic Robustness

[+] Author and Article Information
Chuanfeng Wang1

Donghai Li

Department of Thermal Engineering, Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Institute of Simulation and Control for Thermal Power Engineering,  Tsinghua University, Beijing 100084, Chinalidongh@mail.tsinghua.edu.cn


Corresponding author.

J. Dyn. Sys., Meas., Control 133(6), 061015 (Nov 21, 2011) (8 pages) doi:10.1115/1.4004781 History: Received August 05, 2009; Revised May 08, 2011; Published November 21, 2011; Online November 21, 2011

A tuning method for decentralized PID controllers was developed based on probabilistic robustness for multi-input-multi-output plants, whose parameters vary in a determinate area. The advantage of this method is that the entire uncertainty parameter space can be considered for controller designing. According to model uncertainties, the probabilities of satisfaction for every item of dynamic performance requirements were computed and synthesized as the cost function of genetic algorithms, which was used to optimize the parameters of decentralized PID controllers. Monte Carlo experiments were used to test the control system robustness. Simulations for five multivariable chemical processes were carried out. Comparisons with a standard design method based on nominal conditions indicate that the method presented in this paper has better robustness, and the systems can satisfy the design requirements in a maximal probability.

Copyright © 2011 by American Society of Mechanical Engineers
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Figure 1

Closed feedback control system with decentralized PID controllers

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Figure 2

Probabilistic optimization based on genetic algorithm

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Figure 3

Comparisons for step responses (——for nominal condition,——for parameter uncertainties)

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Figure 4

Comparisons for control actions u(t) (——for nominal condition,——for parameter uncertainties)



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