Towards a Modular Framework for Distributed Model Predictive Control of Nonlinear Neighbor-Affine Systems

Burk, Daniel; Völz, Andreas; Graichen, Knut

doi:10.1109/cdc40024.2019.9029800

Cited by 10 publications

(6 citation statements)

References 12 publications

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“…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%

“…with the desired state It was shown in Burk et al (2019) that the computation time per agent is nearly independent of the system size, whereas in the central MPC case the computation time rises drastically. The simulation results for a system with 40 × 40 agents are given in Fig.…”

Section: Scalable Systemmentioning

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%

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

2021

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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.

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Section: Distributed Model Predictive Controlmentioning

confidence: 99%

Section: Scalable Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2021

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“…Further advantages like fast speed or even real-time capability are further described in [44]. The algorithmic side of GRAMPC is well documented in [47,48]. Now all required components necessary to facilitate predictive formation control that compensates for the effect of a distributed closed-loop network are realized.…”

Section: N]mentioning

confidence: 99%

Self-Organizing Control-Loop Recovery for Predictive Networked Formation Control of Fractionated Spacecraft

et al. 2022

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Going beyond the current trend of cooperating multiple small satellites we arrive at fractionated satellite architectures. Here the subsystems of all satellites directly self-organize and cooperate among themselves to achieve a common mission goal. Although this leads to a further increase of the advantages of the initial trend it also introduces new challenges, one of which is how to perform closed-loop control of a satellite over a network of subsystems. We present a two-fold approach to deal with the two main disturbances, data losses in the network and failure of the controller, in a networked predictive formation control scenario. To deal with data loss an event based networked model predictive control approach is extended to enable it to adapt to changing network conditions. The controller failure detection and compensation approach is tailored for a possibly large network of heterogeneous cooperating actuator- and controller nodes. The self-organized control task redistribution uses an auction-based methodology. It scales well with the number of nodes and allows to optimize for continuing good control performance despite the controller switch. The stability and smooth control behavior of our approach during a self-organized controller failure compensation while also being subject to data losses was demonstrated on a hardware testbed using as mission a formation control scenario.

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“…It was shown in [27] that the computation time per agent is nearly independent of the system size, whereas in the central MPC case the computation time rises drastically. The simulation results for a system with 40 × 40 agents are given in Figure 4.…”

Section: Scalable Systemmentioning

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

Towards a Modular Framework for Distributed Model Predictive Control of Nonlinear Neighbor-Affine Systems

Cited by 10 publications

References 12 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)

Self-Organizing Control-Loop Recovery for Predictive Networked Formation Control of Fractionated Spacecraft

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

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