Fuzzy predictive filters in model predictive control
de Costa Sousa, J.M.; Setnes, M.
Industrial Electronics, IEEE Transactions on
Volume 46, Issue 6, Dec 1999 Page(s):1225 - 1232
Digital Object Identifier 10.1109/41.808014
Summary:The application of model predictive control (MPG) to complex,
nonlinear processes results in a nonconvex optimization problem for
computing the optimal control actions. This optimization problem can be
addressed by discrete search techniques, such as the branch-and-bound
method, which has been successfully applied to MPG. The discretization,
however, introduces a tradeoff between the number of discrete actions
(computation time) and the performance. This paper proposes a solution
to these problems by using a fuzzy predictive filter to construct the
discrete control alternatives. The filter is represented as an adaptive
set of control actions multiplied by a gain factor. This keeps the
number of necessary alternatives low and increases the performance.
Herewith, the problems introduced by the discretization of the control
actions are diminished. The proposed MPC method using fuzzy predictive
filters is applied by the temperature control of an air-conditioned test
room. Simulations and real-time results show the advantages of the
proposed method
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