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Paper details
Number 1 - March 2021
Volume 31 - 2021
A numerically efficient fuzzy MPC algorithm with fast generation of the control signal
Piotr M. Marusak
Abstract
Model predictive control (MPC) algorithms are widely used in practical applications. They are usually formulated as
optimization problems. If a model used for prediction is linear (or linearized on-line), then the optimization problem is a
standard, i.e., quadratic, one. Otherwise, it is a nonlinear, in general, nonconvex optimization problem. In the latter case,
numerical problems may occur during solving this problem, and the time needed to calculate control signals cannot be
determined. Therefore, approaches based on linear or linearized models are preferred in practical applications. A novel,
fuzzy, numerically efficient MPC algorithm is proposed in the paper. It can offer better performance than the algorithms
based on linear models, and very close to that of the algorithms based on nonlinear optimization. Its main advantage is
the short time needed to calculate the control value at each sampling instant compared with optimization-based numerical
algorithms; it is a combination of analytical and numerical versions of MPC algorithms. The efficiency of the proposed
approach is demonstrated using control systems of two nonlinear control plants: the first one is a chemical CSTR reactor
with a van de Vusse reaction, and the second one is a pH reactor.
Keywords
model predictive control, fuzzy systems, fuzzy control, nonlinear control