Neighbor Approximations for Distributed Optimal Control of Nonlinear Networked Systems

Burk, Daniel; Völz, Andreas; Graichen, Knut

doi:10.23919/ecc51009.2020.9143752

Cited by 7 publications

(7 citation statements)

References 13 publications

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“…The expectation is that the additional information about the neighborhood improves the local solution of each agent and thus the convergence behavior of the overall algorithm. This also reduces the number of required ADMM iterations until convergence is reached, which has been confirmed in numerical evaluations in Hentzelt and Graichen (2013) and Burk et al (2020). In practical experience, the reduced number of ADMM iterations can compensate for the increased complexity of the extended OCP which can lead to a significantly decreased computational effort (Burk et al 2020).…”

Section: Neighbor Approximationmentioning

confidence: 64%

“…In the presented DMPC framework, the well-known ADMM algorithm (Boyd et al 2011) is employed in a continuous-time setting (Bestler and Graichen 2019). Note that the formulation of the algorithm is based on previous work (Burk et al 2019(Burk et al , 2020.…”

Section: Distributed Model Predictive Controlmentioning

confidence: 99%

“…In practice, the convergence speed of the ADMM algorithm can be enhanced by anticipating the actions of the neighbors in the own agents optimization. The concept of neighbor approximation was introduced in Hentzelt and Graichen (2013) and extended in Burk et al (2020) and relies on the neighbor affine structure of the dynamics (1b)-(1c) and constraints (1d)-(1h).…”

Section: Neighbor Approximationmentioning

confidence: 99%

“…The presented framework, in the following GRAMPC-D, provides an opensource C++implementation of the ADMM algorithm (Burk et al 2019(Burk et al , 2020 that is capable of solving optimal control problems in a distributed manner with a per-agent computation-time in the millisecond range. The underlying minimization problems are solved with the MPC toolbox GRAMPC that is suitable for embedded hardware implementations.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A modular framework for distributed model predictive control of nonlinear continuous-time systems (GRAMPC-D)

2021

Self Cite

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The modular open-source framework GRAMPC-D for model predictive control of distributed systems is presented in this paper. The modular concept allows to solve optimal control problems in a centralized and distributed fashion using the same problem description. It is tailored to computational efficiency with the focus on embedded hardware. The distributed solution is based on the alternating direction method of multipliers and uses the concept of neighbor approximation to enhance convergence speed. The presented framework can be accessed through C++ and Python and also supports plug-and-play and data exchange between agents over a network.

show abstract

Section: Neighbor Approximationmentioning

confidence: 64%

Section: Distributed Model Predictive Controlmentioning

confidence: 99%

Section: Neighbor Approximationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A modular framework for distributed model predictive control of nonlinear continuous-time systems (GRAMPC-D)

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…In practice, the convergence speed of the ADMM algorithm can be enhanced by anticipating the actions of the neighbors in the own agents optimization. The concept of neighbor approximation was introduced in [19] and extended in [23] and relies on the neighbor affine structure of the dynamics (1b)-(1c) and constraints (1d)-(1h).…”

Section: Neighbor Approximationmentioning

confidence: 99%

A Modular Framework for Distributed Model Predictive Control of Nonlinear Continuous-Time Systems (GRAMPC-D)

Burk¹,

Völz²,

Graichen³

2020

Preprint

Self Cite

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The modular open-source framework GRAMPC-D for model predictive control of distributed systems is presented in this paper. The modular concept allows to solve optimal control problems (OCP) in a centralized and distributed fashion using the same problem description. It is tailored to computational efficiency with the focus on embedded hardware. The distributed solution is based on the Alternating Direction Method of Multipliers (ADMM) and uses the concept of neighbor approximation to enhance convergence speed. The presented framework can be accessed through C ++ and Python and also supports plug-and-play and data exchange between agents over a network.

show abstract

Data-Driven Distributed Optimal Control Using Neighbourhood Optimization for Nonlinear Interconnected Systems

Farzanegan,

Menhaj,

Suratgar

2024

J Optim Theory Appl

View full text Add to dashboard Cite

Neighbor Approximations for Distributed Optimal Control of Nonlinear Networked Systems

Cited by 7 publications

References 13 publications

A modular framework for distributed model predictive control of nonlinear continuous-time systems (GRAMPC-D)

A modular framework for distributed model predictive control of nonlinear continuous-time systems (GRAMPC-D)

A Modular Framework for Distributed Model Predictive Control of Nonlinear Continuous-Time Systems (GRAMPC-D)

Data-Driven Distributed Optimal Control Using Neighbourhood Optimization for Nonlinear Interconnected Systems

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