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Download Dynamic Modeling, Predictive Control and Performance by Biao Huang PDF

By Biao Huang

A common layout approach for version predictive regulate or keep watch over functionality tracking comprises: 1. id of a parametric or nonparametric version; 2. derivation of the output predictor from the version; three. layout of the keep an eye on legislation or calculation of functionality indices in line with the predictor.

Both layout difficulties desire an specific version shape and either require this three-step layout technique. Can this layout method be simplified? Can an particular version be kept away from? With those questions in brain, the authors cast off the 1st and moment step of the above layout strategy, a “data-driven” method within the feel that no conventional parametric versions are used; for this reason, the intermediate subspace matrices, that are received from the method facts and in a different way pointed out as a primary step within the subspace identity equipment, are used at once for the designs. with no utilizing an specific version, the layout technique is simplified and the modelling errors attributable to parameterization is eliminated.

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Extra info for Dynamic Modeling, Predictive Control and Performance Monitoring: A Data-driven Subspace Approach

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Subspace identification involves estimating a basis for the states of the system from the data Hankel matrices. It must be remembered that the states identified using these techniques do not have any physical meaning. The different subspace identification techniques available in the literature also differ in the manner in which the basis of the state space is estimated. The choices for a basis differ in a transformational matrix T that transforms a model {A, B, C, D } into an equivalent model {T −1 AT, T −1B, CT, D } [53].

Ru (n − 2) ⎟ ⎟ .. ⎟ ⎟ . ⎠ ··· ru (0) In general, a necessary condition for consistent estimation of an nth-order linear system is that the input signal is persistently exciting of order 2n [10]. 5 Model Structures A general model structure is given by [11]: yt = Gp (z −1 ; θ)ut + Gl (z −1 ; θ)et where Gp (z −1 ; θ) is the process/plant transfer function, and Gl (z −1 ; θ) is the disturbance transfer function. We assume: −1 −1 ; θ) and G−1 ; θ)Gp (z −1 ; θ) are asymptotically stable.

Yt−1 , ut−1 , yt−2 , ut−2 , . ). Consider the general model structure yt = Gp (z −1 ; θ)ut + Gl (z −1 ; θ)et with the assumption that Gp (0; θ) = 0. 17) 22 2 System Identification: Conventional Approach Thus, the prediction error can be further written as ε(t, θ) = Gp (z −1 ; θ)ut + Gl (z −1 ; θ)et − L1 (z −1 ; θ)yt − L2 (z −1 ; θ)ut = Gp (z −1 ; θ)ut + (Gl (z −1 ; θ) − I)et + et − L1 (z −1 ; θ)yt − L2 (z −1 ; θ)ut = (Gp (z −1 ; θ) − L2 (z −1 ; θ))ut + (Gl (z −1 ; θ) − I − L1 (z −1 ; θ))yt + et = Ψu (z −1 ; θ)ut + Ψy (z −1 ; θ)yt + et Given the conditions Gp (0; θ) = 0, Gl (0; θ) = I, L1 (0; θ) = 0, and L2 (0; θ) = 0, it can be verified that Ψu (0; θ) = 0 Ψy (0; θ) = 0 Namely, both Ψu (z −1 ; θ) and Ψy (z −1 ; θ) have at least one sample time delay.

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