Decentralized switched model-based predictive control for distributed large-scale systems with topology switching

Ahandani, Morteza Alinia; Kharrati, Hamed; Hashemzadeh, Farzad; Baradarannia, Mahdi

doi:10.1016/j.nahs.2020.100912

Cited by 12 publications

(6 citation statements)

References 21 publications

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“…However, as these interconnections get more intricate and the scale of plants gets larger, implementing traditional centralized and decentralized control techniques becomes an uphill task due to the nature of large-scale plants having complex nonlinear dynamics on top of being computationally tiresome to solve, lacking flexibility, and having poor error tolerance . Although process integration improves the operational efficiency of process plants, the large recycling of material and energy that naturally occurs in networked process systems also has an ugly side manifested in the form of time-scale multiplicity in system dynamics. , …”

Section: Introductionmentioning

confidence: 99%

Data-Based Modeling of a Nonexplicit Two-Time Scale Process via Multiple Time-Scale Recurrent Neural Networks

Jian

Zabiri

Ramasamy

2022

Ind. Eng. Chem. Res.

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Modern chemical process plants are typically very complex in nature due to the various material and energy recycling streams, including the implementation of process intensification in order to improve the sustainability factor. Looking at each individual unit operation, there are inherent nonlinear properties that may often involve physical and chemical phenomena occurring on different time scales. In addition, there exist chemical processes that also inherently have dynamics that are multi time-scale in nature. However, designing the control system for those integrated and intensified processes poses significant difficulties because they naturally lead to the dynamics of the network having multiple timescale behaviors and a reduced number of degrees of freedom. The status quo approach to handle multiple time scales would be to separate the plant-wide dynamics into their fast and slow components and implement a hierarchical control structure, but the current practices would lead to computational issues when being applied to model-based controllers due to the inversion of ill-conditioned and stiff differential algebraic equation models under large time-scale separation. To address this issue, this study proposes an alternative approach to modeling multi time-scale processes based on the use of multiple time-scale recurrent neural networks (MTRNNs). The analysis is demonstrated via a benchmark multi time-scale continuous stirred tank reactor (CSTR) case study, using input−output data generated via simulation of nonlinear dynamical equations of the CSTR with large parameters (reciprocal of the singular perturbation small parameter) in the form of 1/ε as the large heat transfer coefficient. The MTRNN model is constructed with groups of neuronal nodes grouped together based on their assigned time constant (also known as decay rate), which leads to an improved prediction performance when compared to other common modeling methods. The prediction performance of the proposed MTRNN model when applied to the multi time-scale CSTR results in an R 2 value of 0.9997, in addition to an average of 75 times lower root mean square error when compared to that of the nonlinear autoregressive network with exogenous inputs (NARX) and transfer function methods, implying its higher potential efficacy in modeling complex multi timescale processes.

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Section: Introductionmentioning

confidence: 99%

Data-Based Modeling of a Nonexplicit Two-Time Scale Process via Multiple Time-Scale Recurrent Neural Networks

Jian

Zabiri

Ramasamy

2022

Ind. Eng. Chem. Res.

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“…A more recent control approach is that of clustering (Ding et al, 2016;Fele et al, 2017;Barreiro-Gomez et al, 2019;Ahandani et al, 2020), which allows dynamic cooperation between time-varying groups of local controllers. Indeed, system partitioning can be considered an offline clustering problem where subsystems are grouped most efficiently according to a given criterion.…”

Section: Introductionmentioning

confidence: 99%

A light clustering model predictive control approach to maximize thermal power in solar parabolic-trough plants

et al. 2021

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“…27,28 Switching topologies, often combined with the design of MPC algorithms, have been studied to account for varying characteristics of the network. [29][30][31][32] Another recently developed approach, named coalitional control, 33,34 is based on the following idea: assuming the network divided into nonoverlapping clusters, each agent accepts to belong to a given cluster until this guarantees it an economic benefit. With specific reference to the clustering of power networks, many other contributions have been proposed, 20,23,[35][36][37] including sparsity-promoting optimization algorithms for damping the interarea oscillations in power networks.…”

Section: Introductionmentioning

confidence: 99%

“…Other approaches rely on social network community detection, 24,25 game theory, 26 or Page Rank 27,28 . Switching topologies, often combined with the design of MPC algorithms, have been studied to account for varying characteristics of the network 29‐32 . Another recently developed approach, named coalitional control , 33,34 is based on the following idea: assuming the network divided into nonoverlapping clusters, each agent accepts to belong to a given cluster until this guarantees it an economic benefit.…”

Section: Introductionmentioning

confidence: 99%

Supervised model predictive control of large‐scale electricity networks via clustering methods

Bella

Klaus

Ferrari-Trecate

et al. 2021

Optim Control Appl Methods

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This article describes a control approach for large-scale electricity networks, with the goal of efficiently coordinating distributed generators to balance unexpected load variations with respect to nominal forecasts. To mitigate the difficulties due to the size of the problem, the proposed methodology is divided in two steps. First, the network is partitioned into clusters, composed of several dispatchable and nondispatchable generators, storage systems, and loads. A clustering algorithm is designed with the aim of obtaining clusters with the following characteristics: (i) they must be compact, keeping the distance between generators and loads as small as possible; (ii) they must be able to internally balance load variations to the maximum possible extent. Once the network clustering has been completed, a two layer control system is designed. At the lower layer, a local model predictive controller is associated to each cluster for managing the available generation and storage elements to compensate local load variations. If the local sources are not sufficient to balance the cluster's load variations, a power request is sent to the supervisory layer, which optimally distributes additional resources available from the other clusters of the network. To enhance the scalability of the approach, the supervisor is implemented relying on a fully distributed optimization algorithm. The IEEE 118-bus system is used to test the proposed design procedure in a nontrivial scenario.

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Decentralized switched model-based predictive control for distributed large-scale systems with topology switching

Cited by 12 publications

References 21 publications

Data-Based Modeling of a Nonexplicit Two-Time Scale Process via Multiple Time-Scale Recurrent Neural Networks

Data-Based Modeling of a Nonexplicit Two-Time Scale Process via Multiple Time-Scale Recurrent Neural Networks

A light clustering model predictive control approach to maximize thermal power in solar parabolic-trough plants

Supervised model predictive control of large‐scale electricity networks via clustering methods

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