|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|4944134||1363983||2018||15 صفحه PDF||سفارش دهید||دانلود کنید|
It poses significant challenge to control Hammerstein-Wiener systems involving modeling nonlinearities. In this paper, a novel data-driven predictive control method based on the subspace identification of Hammerstein-Wiener systems is presented. By reformulating the open- and closed-loop Hammerstein-Wiener model, subspace predictions of the outputs are derived using recursive substitution of the Hankel matrices. The output nonlinearity is presented by polynomial representation and the subspace predictors are obtained using the QR decomposition, together with additional algebra manipulations, where Q is an orthogonal matrix and R is an upper triangular matrix. The predictors are applied to the model predictive controller, wherein the integrated action is successfully incorporated. The effectiveness and feasibility of the proposed controller is also verified by numerical simulation on a fermentation bioreactor system.
Journal: Information Sciences - Volume 422, January 2018, Pages 447-461