Optimal control of a laboratory binary distillation column via regionless explicit MPC

Drgoňa, Ján; Klaučo, Martin; Janeček, Filip; Kvasnica, Michal

doi:10.1016/j.compchemeng.2016.10.003

Cited by 27 publications

(14 citation statements)

References 23 publications

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“…The details of the on-line algorithm are reported in [45]. The resulting control action is identical to the one generated by the region-based e-MPC, i.e., the regionless and region-based implementations bring exactly the same results.…”

Section: E Regionless E-mpcmentioning

confidence: 94%

“…The method does not need to compute or store the critical regions, defined by and . In fact, in the off-line process all the possible active sets {Α 1 , … , Α } that can be locally optimal are considered through the extensive enumeration method in [45], where is the number of regions. A linear program based on the Karush-Kuhn-Tucker (KKT) conditions is solved to determine the feasibility of the candidate active sets.…”

Section: E Regionless E-mpcmentioning

confidence: 99%

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Regionless Explicit Model Predictive Control of Active Suspension Systems With Preview

Theunissen¹,

Sorniotti

Gruber

et al. 2020

IEEE Trans. Ind. Electron.

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Latest advances in road profile sensors make the implementation of pre-emptive suspension control a viable option for production vehicles. From the control side, model predictive control (MPC) in combination with preview is a powerful solution for this application. However, the significant computational load associated with conventional implicit model predictive controllers (i-MPCs) is one of the limiting factors to the widespread industrial adoption of MPC. As an alternative, this paper proposes an explicit model predictive controller (e-MPC) for an active suspension system with preview. The MPC optimization is run offline, and the online controller is reduced to a function evaluation. To overcome the increased memory requirements, the controller uses the recently developed regionless e-MPC approach. The controller was assessed through simulations and experiments on a sport utility vehicle demonstrator with controllable hydraulic suspension actuators. For frequencies <4 Hz, the experimental results with the regionless e-MPC without preview show a ~10% reduction of the root mean square value of the vertical acceleration of the sprung mass with respect to the same vehicle with a skyhook controller. In the same frequency range, the addition of preview improves the heave and pitch acceleration performance by a further 8% to 21%.

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Section: E Regionless E-mpcmentioning

confidence: 94%

Section: E Regionless E-mpcmentioning

confidence: 99%

Regionless Explicit Model Predictive Control of Active Suspension Systems With Preview

Theunissen¹,

Sorniotti

Gruber

et al. 2020

IEEE Trans. Ind. Electron.

Self Cite

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show abstract

“…In the work of Fabro et al, the authors presented an intelligent predictive controller, where a neural network predicts the behavior of the system and a genetic algorithm modifies the controller's parameters based on an optimization problem. In the work of Drgoňa et al, a regionless explicit MPC is designed and utilized in a laboratory binary distillation column, where the construction of critical regions is replaced by a two‐step procedure. In the work of Meidanshahi and Adams, using a mixed‐integer dynamic optimization in a semicontinuous distillation process, an algorithm has been presented to optimize both the structure and tuning parameters of the system.…”

Section: Introductionmentioning

confidence: 99%

“…This paper utilizes the gradient‐based interaction‐prediction approach presented in the work of Sadati and Ramezani to obtain the optimal control for a distillation column. A distillation column is a high‐order system that still receives control engineers' attention to apply their contributions . Because the column consists of several trays (stages), there would be a physical interpretation for system decomposition.…”

Section: Introductionmentioning

confidence: 99%

“…A distillation column is a high-order system that still receives control engineers' attention to apply their contributions. 10,25 Because the column consists of several trays (stages), there would be a physical interpretation for system decomposition. Consequently, the hierarchical control provides a proper framework to improve the system's reliability as well as reducing its complexity and time requirement.…”

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confidence: 99%

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Hierarchical optimal control of a binary distillation column

Ramezani

Sadati

2018

Optim Control Appl Methods

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Summary This paper presents a two‐level optimal control for the binary distillation column. From the control point of view, the binary distillation column is a high‐order system consisting of several interconnected subsystems (trays) that aim to maximize output purity. Controlling the system in a hierarchical manner not only decreases complexity and solution time but also can improve the control structure's reliability. In this study, the distillation column is decomposed into NT second‐order subsystems, each representing one stage (one tray). Accordingly, the cost function is decomposed, and thus, the overall problem is converted into NT lower‐order subproblems where each tray has its own controller. A two‐level interaction prediction approach provides optimal control for the overall system. The task of the first level is to solve the subproblems using the predicted values of the coordination parameters, whereas the second level acts as a coordinator to update the coordination parameters. Simulation results show the capability and efficacy of the proposed two‐level control method in finding the optimal solution with less complexity and lower solution time than those of the centralized method.

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Model Predictive Control

Klaučo

Kvasnica

2019

MPC-Based Reference Governors

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Optimal control of a laboratory binary distillation column via regionless explicit MPC

Cited by 27 publications

References 23 publications

Regionless Explicit Model Predictive Control of Active Suspension Systems With Preview

Regionless Explicit Model Predictive Control of Active Suspension Systems With Preview

Hierarchical optimal control of a binary distillation column

Model Predictive Control

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