A Tuning Scheme for Parameters of Generalized Predictive Controller Based on Mind Evolutionary Algorithm

This paper presents a scheme that the Mind Evolutionary Algorithm (MEA) tunes adaptively parameters of the generalized predictive controller. The value domain of parameters constitutes the solution space of MEA. The cost function of Generalized Predictive Control (GPC), the maximum value of the syst...

Full description

Saved in:
Bibliographic Details
Published in2012 International Conference on Computing, Measurement, Control and Sensor Network pp. 307 - 310
Main Authors Hongge Guo, Keming Xie
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2012
Subjects
Adaption
Aerospace electronics
Educational institutions
Evolutionary computation
Generalized Predictive Control
Mathematical model
Mind Evolutionary Algorithm
Predictive control
Robustness
Tuning
Tuning Parameters
Online AccessGet full text

Cover

More Information
Summary:This paper presents a scheme that the Mind Evolutionary Algorithm (MEA) tunes adaptively parameters of the generalized predictive controller. The value domain of parameters constitutes the solution space of MEA. The cost function of Generalized Predictive Control (GPC), the maximum value of the system output and its decay speed constitute the fitness function of MEA. During the control process, MEA adjusts constantly parameters so that the rapidity and robustness of GPC can be improved. Optimum experiments and simulation experiments show the scheme effectiveness and feasibility.
ISBN:1467320331
9781467320337
DOI:10.1109/CMCSN.2012.74