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Plug-and-Play Monitoring and Performance Optimization for by Hao Luo

By Hao Luo

Dr.-Ing. Hao Luo demonstrates the advancements of complex plug-and-play (PnP) technique tracking and keep an eye on platforms for business automation strategies. With relief of the so-called Youla parameterization, a unique PnP procedure tracking and keep an eye on structure (PnP-PMCA) with modularized elements is proposed. To validate the advancements, a case examine on an business rolling mill benchmark is played, and the real-time implementation on a laboratory brushless DC motor is gifted.

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Let the double coprime factorization of G(z) be chosen as Eqs. e.   A + BF B L ˆ M(z) −Y(z) = F I 0 , ˆ N(z) X(z) C + DF D I   A − LC −(B − LD) −L X(z) Y(z) = F I 0 , ˆ ˆ −N(z) M(z) −D I C where F and L are chosen such that A + BF and A − LC are both stable. Then, the standard feedback control loop Fig. 2 can be equivalently realized as the control structure shown in Fig. 3, in which the observer-based residual generator has the following state-space representation: xe,k+1 = ˆ ek = re,k = rk := Axe,k + Buk + Lre,k , Cxe,k + Duk , ek − ˆ ek = y ˆ k − yk , yk − y ˆk = −re,k .

3: Generalized form of feedback control system (Zhou, 1996) Consider the generalized form of the feedback control system as shown in Fig. g. g. ). 15) where G22 (z) = A B2 C2 D22 = Gyu (z) = A B C D . The stabilizing problem is then to find a feedback controller K(z) such that the closedloop system is internally stable. 2: Suppose the given plant Gyu (z) is stabilizable and detectable, then the generalized feedback control system shown in Fig. e. Gyu (z). According to the above lemma, the stabilizing controller for the generalized plant G(z) depends only on the plant Gyu .

23) namely, the H∞ -norm of the transfer function matrix from the exogenous inputs to the control objectives has to be minimized under the constraint that the closed-loop is internally stable. 5 Model uncertainty and robustness Most control schemes are based on the use of the model of an actual plant. A mathematical model provides a map from inputs to responses. For model-based control design, a good model should be simple enough to facilitate the design, while complex enough to match the responses of the true plant so that the design could actually work.

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