Abstract A novel distributed model predictive control method for linear discrete-time systems is considered. The method can handle coupled constraints as well as coupled objective functions. For the decomposed optimal...
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Abstract A novel distributed model predictive control method for linear discrete-time systems is considered. The method can handle coupled constraints as well as coupled objective functions. For the decomposed optimal control problems embedded in the distributed controller additional linear information on the full optimal control problem is added. On each horizon, the resulting coordinated optimal control problem is solved iteratively. Prices and resources are calculated simultaneously. A convergence analysis is presented as well as a case study for an unstable process. There, an almost optimal control sequence is achieved after only one iteration.
Being one of the most approached subjects in the field of chemical engineering, a lot of numerical modeling methods for the distillation columns were presented in scientific papers. This means that in order to find a ...
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Based on a closed-loop step response test, a low-order model identification method is proposed to facilitate on-line controller tuning for load disturbance rejection in industrial engineering practices. By introducing...
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Based on a closed-loop step response test, a low-order model identification method is proposed to facilitate on-line controller tuning for load disturbance rejection in industrial engineering practices. By introducing a damping factor to the closed-loop step response for realizing the Laplace transform, a frequency response estimation algorithm is given in terms of the closed-loop system configuration for operation. Correspondingly, a model identification algorithm is analytically derived for obtaining a low-order process model of first-order-plus-dead-time (FOPDT). Another identification algorithm is developed for improving model fitting accuracy over a specified frequency range interested to controller tuning, specifically detailed for obtaining the most widely used FOPDT and second-order-plus-dead-time (SOPDT) models, based on a weighted least-squares fitting of the process frequency response points estimated in the specified frequency range. Using the identified models, a model-based controller design and tuning method is proposed for improving load disturbance rejection. Illustrative examples from recent references are used to demonstrate the effectiveness and merits of the proposed identification algorithms and controller tuning method.
Abstract This paper presents a generalization of the model reduction method proper orthogonal decomposition to systems of differential-algebraic equations of arbitrary index. It is known that proper orthogonal decompo...
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Abstract This paper presents a generalization of the model reduction method proper orthogonal decomposition to systems of differential-algebraic equations of arbitrary index. It is known that proper orthogonal decomposition generalizes the method of empirical balanced truncation for linear time-invariant systems. This property will be preserved for differential-algebraic equation systems of arbitrary index. This is important for the application of proper orthogonal decomposition in control, where the input-output behaviour should be approximated accurately, which is a well-known property of balanced-truncated systems.
Among all kinds of studies on boiling processes,the efficient and accurate estimation of the local heat flux distribution at the boiling surface,beyond its average information which can be obtained by traditional appr...
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Among all kinds of studies on boiling processes,the efficient and accurate estimation of the local heat flux distribution at the boiling surface,beyond its average information which can be obtained by traditional approaches,is a crucial prerequisite in modern modeling of boiling heat *** accurate predictions,the heat flux estimation task is formulated as a three-dimensional(3D) inverse heat conduction problem(IHCP).In case of a complex heater geometry,realistic boundary conditions and a nonuniformly measurement configuration,the solution of the 3D IHCP considered leads to a large-scale PDE-constrained optimization ***,regularization-based techniques have to be applied to tackle the inherent severe ill-posedness of the 3D *** paper gives a brief summary of our recent developments in the efficient solution of the 3D IHCP which accomplishes the task of heat flux reconstruction in pool *** incorporates an iterative regularization based on a conjugate gradient method for the normal equations(CGNE) and a multi level adaptive computational *** novel method can efficiently cope with both idealized (high-resolution) and limited point-wise experimental data which are commonly available in most heat transfer *** method has been applied to the evaluation of real temperature data obtained from two novel pool boiling *** the first time,the computational effort of such tasks reduces to the order of minutes on standard desktop computers.
Based on a two-dimensional (2D) system description of a batch process, a robust closed-loop iterative learning control (ILC) scheme is proposed for batch processes with time-varying uncertainties. An important merit i...
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Being one of the most approached subjects in the field of chemical engineering, a lot of numerical modeling methods for the distillation columns were presented in scientific papers. This means that in order to find a ...
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Being one of the most approached subjects in the field of chemical engineering, a lot of numerical modeling methods for the distillation columns were presented in scientific papers. This means that in order to find a model that would suit a specific process requires a considerable amount of time. This paper will present a general first principle model that aims at providing users with a framework that can be easily adapted for any type of distillation process. In order to prove these assumptions an isotopic distillation process for the enrichment of 13 C has been selected.
We present a method to improve the performance of nonlinear model predictive control (NMPC) by compromising between the time delay caused by a computational algorithm and the accuracy of the resulting control law in o...
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ISBN:
(纸本)9781612848006
We present a method to improve the performance of nonlinear model predictive control (NMPC) by compromising between the time delay caused by a computational algorithm and the accuracy of the resulting control law in order to achieve best possible closed-loop performance. The main feature of the method is an a-priori error approximation derived for the neighboring-extremal update (NEU) algorithm, a fast NMPC algorithm presented recently by the authors. The error estimate provides the deviation of the current control trajectory from the (unknown) optimal control trajectory. The a-priori error estimator is incorporated in an on-line decision making process which simultaneously decides on the quality of the computed controls and the computational delay. In particular, the optimal number of QP iterations in an SQP strategy is determined on each horizon prior to the computation of the current control move.
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