Event-Triggered Communication in Cooperative, Adaptive Model Predictive Control of a Nuclear Power Plant’s Turbo–Generator Set

Sokólski, Paweł; Rutkowski, Tomasz A.; Ceran, Bartosz; Złotecka, Daria; Horla, Dariusz

doi:10.3390/en16134962

Cited by 2 publications

(2 citation statements)

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“…Model Predictive Control (MPC) refers to an advanced control strategy in which a dynamical model of the considered process is utilized online to predict the future process state and an optimization procedure finds the best possible control action to minimize the predefined control quality index [1]. MPC algorithms have been used for years in process control; typical applications include chemical reactors [2], olefin metathesis processes [3], distillation towers [4] and power plants [5]. Nowadays, as a result of the availability of fast and relatively cheap hardware platforms necessary to carry out all online calculations, MPC algorithms are used in smart buildings [6] and several embedded systems; example applications include autonomous ground vehicle [7], autonomous driving vehicle [8], planning vehicle-parking trajectories for vertical parking spaces [9] and quadrotors [10,11].…”

Section: Introductionmentioning

confidence: 99%

Fast Nonlinear Predictive Control Using Classical and Parallel Wiener Models: A Comparison for a Neutralization Reactor Process

Nebeluk,

Ławryńczuk

2023

Sensors

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The Wiener model, composed of a linear dynamical block and a nonlinear static one connected in series, is frequently used for prediction in Model Predictive Control (MPC) algorithms. The parallel structure is an extension of the classical Wiener model; it is expected to offer better modeling accuracy and increase the MPC control quality. This work discusses the benefits of using the parallel Wiener model in MPC. It has three objectives. Firstly, it describes a fast MPC algorithm in which parallel Wiener models are used for online prediction. In the presented approach, sophisticated trajectory linearization is performed online, which leads to computationally fast quadratic optimization. The second objective of this work is to study the influence of the model structure on modeling accuracy. The well-known neutralization benchmark process is considered. It is shown that the parallel Wiener models in the open-loop mode generate significantly fewer errors than the classical structure. This work’s third objective is to validate the efficiency of parallel Wiener models in closed-loop MPC. For the neutralization process, it is demonstrated that parallel models demonstrate better control quality using various indicators, but the difference between the classical and parallel models is not significant.

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Section: Introductionmentioning

confidence: 99%

Fast Nonlinear Predictive Control Using Classical and Parallel Wiener Models: A Comparison for a Neutralization Reactor Process

Nebeluk,

Ławryńczuk

2023

Sensors

View full text Add to dashboard Cite

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“…The paper by Sokólski et al, "Event-Triggered Communication in Cooperative, Adaptive Model Predictive Control of a Nuclear Power Plant's Turbo-Generator Set", investigates the problem of ensuring optimal communication between the components of a cooperating control system formed by two MPC controllers for nuclear power plant turbines using online least recursive squares identification [3]. The main goal of this paper was to propose a change in the method of information exchange in the case of asynchronous communication between control system components as well as prove its appropriateness for the chosen solutions.…”

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confidence: 99%