Distributed MPC with Prediction of Time-Varying Communication Delay

Hahn, Jannik; Schoeffauer, Richard; Wunder, Gerhard; Stursberg, Olaf

doi:10.1016/j.ifacol.2018.12.039

Cited by 14 publications

(6 citation statements)

References 13 publications

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“…In other words it predicts the communication delays. Signaling these communication delays ahead of the arrival of the actual data to the corresponding RMPCs facilitates them to enhance their control performance, as was first shown in [1].…”

Section: Introductionmentioning

confidence: 95%

A Linear Algorithm for Reliable Predictive Network Control

Schoeffauer

Wunder

2018

2018 IEEE Globecom Workshops (GC Wkshps)

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This paper introduces a novel control approach for network scheduling and routing that is predictive and reliable in its nature, yet builds upon a linear program, making it fast in execution. First, we describe the canonical system model and how we expand it to be able to predict the success of transmissions. Furthermore, we define a notion of reliability and then explain the algorithm. With extended simulations, we demonstrate the gains in performance over the well known MaxWeight policy.

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Section: Introductionmentioning

confidence: 95%

A Linear Algorithm for Reliable Predictive Network Control

Schoeffauer

Wunder

2018

2018 IEEE Globecom Workshops (GC Wkshps)

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“…b) Model Predictive Control (MPC): MPC has found use in several domains that are related to this work, including controlling distributed multi-agent systems [27], [28] and distributed systems with time delays [20], [13]. Recently, MPC has also been applied to robotics applications such as trajectory tracking [15], vehicle control [5], flight control [2], and cooperative landing [25].…”

Section: Related Work A) Synthesis Techniques For Multi-robot Systemsmentioning

confidence: 99%

OneVision: Centralized to Distributed Controller Synthesis with Delay Compensation

Wei¹,

Li²,

Chaudhuri³

et al. 2021

Preprint

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We propose a new algorithm to simplify the controller development for distributed robotic systems subject to external observations, disturbances, and communication delays. Unlike prior approaches that propose specialized solutions to handling communication latency for specific robotic applications, our algorithm uses an arbitrary centralized controller as the specification and automatically generates distributed controllers with communication management and delay compensation. We formulate our goal as nonlinear optimal controlusing a regret minimizing objective that measures how much the distributed agents behave differently from the delay-free centralized response-and solve for optimal actions w.r.t. local estimations of this objective using gradient-based optimization. We analyze our proposed algorithm's behavior under a linear time-invariant special case and prove that the closed-loop dynamics satisfy a form of input-to-state stability w.r.t. unexpected disturbances and observations. Our experimental results on both simulated and real-world robotic tasks demonstrate the practical usefulness of our approach and show significant improvement over several baseline approaches.

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“…By substituting ( 14) in (12), the constraints ( 13) and ( 15) are combined into the set of all admissible input trajectories (c.f. [33], [34]):…”

Section: Distributed Control Systemmentioning

confidence: 99%

“…is the uncertainty set of the nominal input trajectory of subsystem i (for exact computation of γi k c.f. [33], [34]). The current state, nominal input trajectory, and uncertainty set determine the behavior of subsystem i for the next H timesteps, and are communicated to all neighboring subsystems.…”

Section: Solving (19) Yields the Nominal Input Trajectory ṽImentioning

confidence: 99%

“…Recent work in [33] has sketched the idea of using predictive controllers for both the minimization of network delays, and the control of distributed plants under consideration of predicted AoI. That paper, however, neither detailed the network control scheme, nor the interface between network and subsystem controllers, nor the stability of the overall scheme.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using AoI Forecasts in Communicating and Robust Distributed Model-Predictive Control

Hahn¹,

Schoeffauer²,

Wunder³

et al. 2021

Preprint

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In order to enhance the performance of cyberphysical systems, this paper proposes the integrated design of distributed controllers for distributed plants and the control of the communication network. Conventional design methods use static interfaces between both entities and therefore rely on worst-case estimations of communication delay, often leading to conservative behavior of the overall system. By contrast, the present approach establishes a robust distributed model-predictive control scheme, in which the local subsystem controllers operate under the assumption of a variable communication schedule that is predicted by a network controller. Using appropriate models for the communication network, the network controller applies a predictive network policy for scheduling the communication among the subsystem controllers across the network. Given the resulting timevarying predictions of the age of information, the paper shows under which conditions the subsystem controllers can robustly stabilize the distributed system. To illustrate the approach, the paper also reports on the application to a vehicle platooning scenario.

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Distributed MPC with Prediction of Time-Varying Communication Delay

Cited by 14 publications

References 13 publications

A Linear Algorithm for Reliable Predictive Network Control

A Linear Algorithm for Reliable Predictive Network Control

OneVision: Centralized to Distributed Controller Synthesis with Delay Compensation

Using AoI Forecasts in Communicating and Robust Distributed Model-Predictive Control

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