Nonlinear model predictive control of a packed distillation column

Patwardhan, Ashutosh A.; Edgar, Thomas F.

doi:10.1021/ie00022a018

Cited by 28 publications

(10 citation statements)

References 27 publications

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“…In the simultaneous solution [ER90,PE93,SK99] the differential equations are transformed to algebraic equations which are solved in addition to the nonlinear equality constraints in the optimization. The decision variables includes both the model states and control signals and the model equations are appended to the optimization problem as equality constraints.…”

Section: Simultaneous Methodsmentioning

confidence: 99%

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Echtzeit-Implementierung von NMPC

Löw¹,

Obradović

2018

atp

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Nonlinear Model Predictive Control (NMPC) is an aspiring control method for the implementation of advanced controller behavior. The present work shows the symbolic math implementation of a mechatronic system model containing aerodynamic nonlinearities modeled by Feedforward Neural Networks. Gradients for the optimization are obtained efficiently by exploiting the feedforward property of the Neural Networks and symbolic computation. Current research on the implementation of damage metrics into the cost function is stated briefly. In order to achieve real-time capability, the method Real-time Iteration is used.

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Section: Simultaneous Methodsmentioning

confidence: 99%

“…The NMPC schemes proposed in literature use models developed from first-principles [BS89,PE93] or models identified from input-output data (black-box model or empirical model) [DFL94,SMc97,ZGS98]. Both approaches have been used successfully in MPC applications.…”

Section: Internal Model Formulationmentioning

confidence: 99%

Echtzeit-Implementierung von NMPC

Löw¹,

Obradović

2018

atp

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“…The remain estimation error is further corrected via the output disturbance model dk where in the model nonlinearity is included (see Eq. (9). Note, this is a standard treatment in offset-free MPC and has been recognised as a sufficient alternative of traditional state estimation approaches in MPC [26].…”

Section: Predictive Control Formulationmentioning

confidence: 99%

“…The internal Model in NMPC can be based on the physical laws governing the behaviour of the true system and often referred to as a first-principle model [8,9]. Alternatively the model is derived from measurements of input and output data from the real plant.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear model predictive control for the ALSTOM gasifier

Al-Seyab¹,

Cao²

2006

Journal of Process Control

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In this work a nonlinear model predictive control based on Wiener model has been developed and used to control the ALSTOM gasifier. The 0% load condition was identified as the most difficult case to control among three operating conditions. A linear model of the plant at 0% load is adopted as a base model for prediction. A nonlinear static gain represented by a feedforward neural network was identified for a particular output channel-namely, fuel gas pressure, to compensate its strong nonlinear behaviour observed in open loop simulations. By linearising the neural network at each sampling time, the static nonlinear model provides certain adaptation to the linear base model at all other load conditions. The resulting controller showed noticeable performance improvement when compared with pure linear model based predictive control. 2006 Elsevier Ltd. All rights reserved.

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“…Also, the well-known generalized predictive control has been used instead of the conventional adaptive control algorithms in order to improve the robustness of the resultant control system. This algorithm has been widely accepted as an excellent robust adaptive control for different processes with varied order, delay, parameters, and non-minimum-phase as encountered in this study [19][20][21][22]. Consequently, a qualified system can be developed to control the full penetration using the proposed top-side sensor based on the analysis and dynamic modeling.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Analysis and Identification of Gas Tungsten Arc Welding Process for Weld Penetration Control

Zhang

Kovacevic

1996

Journal of Engineering for Industry

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In this study, gas tungsten arc welding is analyzed and modeled as a 2-input (welding current and arc length) 2-output (weld depression and width) multivariable process. Experiments under a number of typical welding conditions are performed to excite and identify the process characteristics and variations. It is observed that the model parameters vary in a large range with the experimental conditions. A real-time model frame with only a few parameters to be identified on-line is proposed. Based on the obtained models, the process characteristics in terms of inertia, delay, nonminimum phase, and coupling are given. These characteristics suggest an adaptive predictive decoupling control algorithm. By designing and implementing the suggested control algorithm with the real-time model, excellent results have been achieved for both simulation and practical control. This shows that the dynamic analysis and identification provide sufficient process information for design of the control system.

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Nonlinear model predictive control of a packed distillation column

Cited by 28 publications

References 27 publications

Echtzeit-Implementierung von NMPC

Echtzeit-Implementierung von NMPC

Nonlinear model predictive control for the ALSTOM gasifier

Dynamic Analysis and Identification of Gas Tungsten Arc Welding Process for Weld Penetration Control

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