A multivariable end-point state weighted generalized predictive controller for the Bridgman crystal furnace TITUS is developed. A multivariable temperature prediction is done by a reduced dynamic representation using a matrix polynomial representation of the plant. The theoretical equivalence between the proposed multivariable control and Kalman filter state prediction is proven. It is shown, that the incorporation of the stochastic model improves the control performance.

Multivariable end-point weighted generalized predictive control of a space crystal furnace

Closed loop identification method using a subspace approach

A three stage identification method for robust control design

A new Generalized Predictive Control algorithm in a decentralized controller framework is presented. Using a new formulation of output end-point weightings the nominal stability is guaranteed. Robust stability with respect to additive plant uncertainty is assured by an IMC scheme. Using the method of finite elements and a description of composite subsystems respectively the dynamical behavior in the heating area of the crystal furnace TITUS is modeled. A final simulation depicts to a more accurate temperature gradient tracking.

A new predictive controller with guaranteed nominal and robust stability

Modelling, identification and performance analysis of a glass fibre bushing

Identification in closed loop is demanded for open loop unstable plants and may give in general more suitable models for control design [Sch92]. To obtain a parametric model description the closed loop data is used by a standard open loop estimation algorithm. This may lead to a mismatch because the noise acting at the output corrupts the plant input too. A reconstruction of the noise free plant input was proposed by [VSdB95], Our proposed algorithm uses an additional step to reconstruct an undisturbed plant output. In a first step a transfer function estimation (alpha-sensitivity) is used to construct the noise free plant input by help of the reference sequence. In a second step an additional transfer function (gamma-sensitivity) is estimated to reconstruct the noise free plant output. A third step uses standard open loop techniques to estimates the model parameters. It is shown by an elaborate simulation example that this procedure provides more accurate results.

http://ieeexplore.ieee.org/iel7/7055173/7081895/07082674.pdf

Three stage procedure for closed loop identification

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Three stage procedure for closed loop identification