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Fuzzy model predictive control

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4 Author(s)
Huang, Y.L. ; Dept. of Chem. Eng. & Mater. Sci., Wayne State Univ., Detroit, MI, USA ; Lou, H.H. ; Gong, J.P. ; Edgar, T.F.

A fuzzy model predictive control (FMPC) approach is introduced to design a control system for a highly nonlinear process. In this approach, a process system is described by a fuzzy convolution model that consists of a number of quasi-linear fuzzy implications. In controller design, prediction errors and control energy are minimized through a two-layered iterative optimization process. At the lower layer, optimal local control policies are identified to minimize prediction errors in each subsystem. A near optimum is then identified through coordinating the subsystems to reach an overall minimum prediction error at the upper layer. The two-layered computing scheme avoids extensive online nonlinear optimization and permits the design of a controller based on linear control theory. The efficacy of the FMPC approach is demonstrated through three examples

Published in:

Fuzzy Systems, IEEE Transactions on  (Volume:8 ,  Issue: 6 )