An iterative scheme for distributed model predictive control using Fenchel's duality

Doan, Minh Dang; Keviczky, Tamás; Schutter, Bart De

doi:10.1016/j.jprocont.2010.12.009

Cited by 40 publications

(28 citation statements)

References 29 publications

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“…The underlying optimization algorithm in [8], although being implemented in a distributed way, is proved to achieve the global optimum with an O( 1 k 2 ) convergence rate, where k is the iteration number. This is a significant improvement compared to the distributed MPC methods presented in [5,4,9,19], which achieve an O(…”

Section: Introductionmentioning

confidence: 94%

A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley

Doan

Giselsson

Keviczky

et al. 2013

Control Engineering Practice

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A distributed model predictive control (DMPC) approach based on distributed optimization is applied to the power reference tracking problem of a hydro power valley (HPV) system. The applied optimization algorithm is based on accelerated gradient methods and achieves a convergence rate of O 1 k 2 , where k is the iteration number. Major challenges in the control of the HPV include a nonlinear and large-scale model, nonsmoothness in the power-production functions, and a globally coupled cost function that prevents distributed schemes to be applied directly.We propose a linearization and approximation approach that accommodates the proposed the DMPC framework and provides very similar performance compared to a centralized solution in simulations. The provided numerical studies also suggest that for the sparsely interconnected system at hand, the distributed algorithm we propose is faster than a centralized state-of-the-art solver such as CPLEX.

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Section: Introductionmentioning

confidence: 94%

A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley

Doan

Giselsson

Keviczky

et al. 2013

Control Engineering Practice

Self Cite

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“…Primal decomposition refers to the decomposition of the original problem, while dual decomposition manipulates the dual formulation. Dual decomposition has been used in distributed MPC algorithms [23], [24], [25], [26] when the cost function is quadratic and separable.…”

Section: Comparison To Decomposition Algorithms A) Primal and Dualmentioning

confidence: 99%

A Distributed Optimization Algorithm for the Predictive Control of Smart Grids

Braun

Grüne

Kellett

et al. 2016

IEEE Trans. Automat. Contr.

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Abstract-In this paper, we present a hierarchical, iterative distributed optimization algorithm, and show that the algorithm converges to the solution of a particular global optimization problem. The motivation for the distributed optimization problem is the predictive control of a smart grid, in which the states of charge of a network of residential-scale batteries are optimally coordinated so as to minimize variability in the aggregated power supplied to/from the grid by the residential network. The distributed algorithm developed in this paper calls for communication between a central entity and an optimizing agent associated with each battery, but does not require communication between agents. The distributed algorithm is shown to achieve the performance of a large-scale centralized optimization algorithm, but with greatly reduced communication overhead and computational burden. A numerical case study using data from an Australian electricity distribution network is presented to demonstrate the performance of the distributed optimization algorithm.

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“…Extension of DMPC to nonlinear systems is also an active research area, such as an effective nonlinear DMPC based on Lyapunov-based MPC, which follows similar information exchange mechanism as FC-MPC [7]. Interest in distributed MPC has increased in recent years and various novel techniques and implementations have been reported [8][9][10][11][12][13][14][15][16][17][18][19][20]. In literature, centralized MPC was utilized to deal with multi-rate sampling process in [21], as well as distributed MPC in [22,23].…”

Section: Introductionmentioning

confidence: 99%

System identification and distributed control for multi-rate sampled systems

Shao

Çınar

2015

Journal of Process Control

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Keywords:Multi-rate sampling Subspace identification Multi-rate distributed model predictive control Agent-based systems Tennessee Eastman challenge problem a b s t r a c t System outputs with different sampling times may challenge traditional subspace identification methods to generate accurate process models and consequently provide model-based control systems that may not be very effective. The multi-rate identification problem is addressed by dividing the multi-rate sampled system into different subsystems, and a multi-rate distributed model predictive control technique is proposed to control such systems. The performance of the proposed method is evaluated and illustrated by modeling and controlling the Tennessee Eastman challenge problem.

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An iterative scheme for distributed model predictive control using Fenchel's duality

Cited by 40 publications

References 29 publications

A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley

A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley

A Distributed Optimization Algorithm for the Predictive Control of Smart Grids

System identification and distributed control for multi-rate sampled systems

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