Model predictive satisficing fuzzy logic control

Model-predictive control, which is an alternative to conventional optimal control, provides controller solutions to many constrained and nonlinear control problems. However, even when a good model is available, it may be necessary for an expert to specify the relationship between local model predict...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems 1999-06, Vol.7 (3), p.319-332
Main Authors: Goodrich, M.A., Stirling, W.C., Frost, R.L.
Format: Article
Language:English
Subjects:
Control systems
Design engineering
Dynamical systems
Fuzzy
Fuzzy control
Fuzzy logic
Fuzzy systems
Gain
Mathematical models
Nonlinear control systems
Nonlinear systems
Optimal control
Predictive models
Robust control
System performance
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Summary:Model-predictive control, which is an alternative to conventional optimal control, provides controller solutions to many constrained and nonlinear control problems. However, even when a good model is available, it may be necessary for an expert to specify the relationship between local model predictions and global system performance. We present a satisficing fuzzy logic controller that is based on a receding control horizon, but which employs a fuzzy description of system consequences via model predictions. This controller considers the gains and losses associated with each control action, is compatible with robust design objectives, and permits flexible defuzzifier design. We demonstrate the controller's application to representative problems from the control of uncertain nonlinear systems.
ISSN:1063-6706
1941-0034
DOI:10.1109/91.771087