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PSO based Hammerstien Modeling and Predictive Control of a Nonlinear Multivariable Boiler
 

Summary: PSO based Hammerstien Modeling and Predictive Control of a Nonlinear
Multivariable Boiler
H.N. Al-Duwaisha
, S.Z. Rizvib,1
, M.S. Yousufc
, M. Nazmulkarimd
aDept. of Electrical Engineering, King Fahd Univ. of Petroleum & Minerals, P.O.Box 667, Dhahran 31261, Saudi Arabia
bDept. of Electrical Engineering, King Fahd Univ. of Petroleum & Minerals, P.O.Box 76, Dhahran 31261, Saudi Arabia
cDept. of Electrical Engineering, King Fahd Univ. of Petroleum & Minerals, P.O.Box 7136, Dhahran 31261, Saudi Arabia
dDept. of Chemical Engineering, Texas Tech University, 6th and Canton, Mail Stop 3121, TX, USA
Abstract
Controller design of nonlinear processes is a task of imminent industrial significance. However, successful controller
design requires a proper model that describes the process throughout its operating range. This paper uses Hammerstein
model to model an industrial boiler from sampled data collected at Abbott Power Plant in Campaign, IL. Neural
networks and state-space model are used to model the nonlinearities and the dynamics of the system respectively.
Validation results using computer simulation demonstrate the good fit and concordance of predicted outputs with
actual data. A Model Predictive Control (MPC) approach is taken to design a controller for the boiler plant with
the aid of the developed model. Simulation results at the end demonstrate successful control of the plant from one
operating point to the other. The use of Particle Swarm Optimization (PSO) plays an important role in training the
neural network and in finding optimum control signals to steer the plant from one operating point to another.

  

Source: Al-Duwaish, Hussain N. - Electrical Engineering Department, King Fahd University of Petroleum and Minerals

 

Collections: Computer Technologies and Information Sciences; Engineering