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Synchronization-Based Cooperative Distributed Model Predictive Control

Julius Beerwerth, Maximilian Kloock, Bassam Alrifaee

TL;DR

An iterative algorithm called Synchronization-Based Cooperative Distributed Model Predictive Control, which consists of computing the optimal control inputs for each agent and synchronizing the predicted states across all agents is presented.

Abstract

Distributed control algorithms are known to reduce overall computation time compared to centralized control algorithms. However, they can result in inconsistent solutions leading to the violation of safety-critical constraints. Inconsistent solutions can arise when two or more agents compute concurrently while making predictions on each others control actions. To address this issue, we propose an iterative algorithm called Synchronization-Based Cooperative Distributed Model Predictive Control, which we presented in [1]. The algorithm consists of two steps: 1. computing the optimal control inputs for each agent and 2. synchronizing the predicted states across all agents. We demonstrate the efficacy of our algorithm in the control of multiple small-scale vehicles in our Cyber-Physical Mobility Lab.

Synchronization-Based Cooperative Distributed Model Predictive Control

TL;DR

An iterative algorithm called Synchronization-Based Cooperative Distributed Model Predictive Control, which consists of computing the optimal control inputs for each agent and synchronizing the predicted states across all agents is presented.

Abstract

Distributed control algorithms are known to reduce overall computation time compared to centralized control algorithms. However, they can result in inconsistent solutions leading to the violation of safety-critical constraints. Inconsistent solutions can arise when two or more agents compute concurrently while making predictions on each others control actions. To address this issue, we propose an iterative algorithm called Synchronization-Based Cooperative Distributed Model Predictive Control, which we presented in [1]. The algorithm consists of two steps: 1. computing the optimal control inputs for each agent and 2. synchronizing the predicted states across all agents. We demonstrate the efficacy of our algorithm in the control of multiple small-scale vehicles in our Cyber-Physical Mobility Lab.
Paper Structure (5 sections, 1 theorem, 2 equations, 4 figures, 3 algorithms)

This paper contains 5 sections, 1 theorem, 2 equations, 4 figures, 3 algorithms.

Table of Contents

  1. Introduction
  2. Background
  3. The SCDMPC Algorithm
  4. Evaluation
  5. Conclusion

Key Result

theorem 1

The synchronization converges to a solution if and only if each coupling sub-graph $_{i}$ contains a spanning tree.

Figures (4)

  • Figure 1: Example for (a) a coupling graph and (b) the corresponding coupling sub-graph for agent 1.
  • Figure 2: Visualization of the formation building scenario.
  • Figure 3: The average cumulative path and speed deviations of mycmpc and myscdmpc for different numbers of cav.
  • Figure 4: The maximum computation time of mycmpc and myscdmpc for different numbers of cav.

Theorems & Definitions (1)

  • theorem 1