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Adaptive Genetic Selection based Pinning Control with Asymmetric Coupling for Multi-Network Heterogeneous Vehicular Systems

Weian Guo, Ruizhi Sha, Li Li, Lun Zhang, Dongyang Li

TL;DR

This work proposes an adaptive genetic algorithm tailored to select optimal pinning nodes, effectively balancing LMI constraints while prioritizing overlapping nodes to enhance control efficiency, offering practical implications for deploying large-scale intelligent transportation systems.

Abstract

To alleviate computational load on RSUs and cloud platforms, reduce communication bandwidth requirements, and provide a more stable vehicular network service, this paper proposes an optimized pinning control approach for heterogeneous multi-network vehicular ad-hoc networks (VANETs). In such networks, vehicles participate in multiple task-specific networks with asymmetric coupling and dynamic topologies. We first establish a rigorous theoretical foundation by proving the stability of pinning control strategies under both single and multi-network conditions, deriving sufficient stability conditions using Lyapunov theory and linear matrix inequalities (LMIs). Building on this theoretical groundwork, we propose an adaptive genetic algorithm tailored to select optimal pinning nodes, effectively balancing LMI constraints while prioritizing overlapping nodes to enhance control efficiency. Extensive simulations across various network scales demonstrate that our approach achieves rapid consensus with a reduced number of control nodes, particularly when leveraging network overlaps. This work provides a comprehensive solution for efficient control node selection in complex vehicular networks, offering practical implications for deploying large-scale intelligent transportation systems.

Adaptive Genetic Selection based Pinning Control with Asymmetric Coupling for Multi-Network Heterogeneous Vehicular Systems

TL;DR

This work proposes an adaptive genetic algorithm tailored to select optimal pinning nodes, effectively balancing LMI constraints while prioritizing overlapping nodes to enhance control efficiency, offering practical implications for deploying large-scale intelligent transportation systems.

Abstract

To alleviate computational load on RSUs and cloud platforms, reduce communication bandwidth requirements, and provide a more stable vehicular network service, this paper proposes an optimized pinning control approach for heterogeneous multi-network vehicular ad-hoc networks (VANETs). In such networks, vehicles participate in multiple task-specific networks with asymmetric coupling and dynamic topologies. We first establish a rigorous theoretical foundation by proving the stability of pinning control strategies under both single and multi-network conditions, deriving sufficient stability conditions using Lyapunov theory and linear matrix inequalities (LMIs). Building on this theoretical groundwork, we propose an adaptive genetic algorithm tailored to select optimal pinning nodes, effectively balancing LMI constraints while prioritizing overlapping nodes to enhance control efficiency. Extensive simulations across various network scales demonstrate that our approach achieves rapid consensus with a reduced number of control nodes, particularly when leveraging network overlaps. This work provides a comprehensive solution for efficient control node selection in complex vehicular networks, offering practical implications for deploying large-scale intelligent transportation systems.

Paper Structure

This paper contains 34 sections, 2 theorems, 60 equations, 12 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Description and Analysis
  4. Pinning Consensus Problem in a Single Vehicular Network System
  5. Pinning Consensus in Multi-Network Heterogeneous Vehicular Systems
  6. System Model
  7. Problem Statement
  8. Assumptions
  9. Formulation of the Optimization Problem
  10. Constraints Analysis
  11. Stability Constraint
  12. Binary Variable Constraint
  13. Positive Definiteness of Weighting Matrix
  14. Control Gain Constraint
  15. Challenges Arising from Constraint Conditions
  16. ...and 19 more sections

Key Result

Theorem 1

Consider the continuous-time vehicular network system eqn:base_model consisting of $N$ vehicles, where the adjacency matrix $G$ leads to a Laplacian matrix $L$ that is stable (i.e., all non-zero eigenvalues of $L$ have positive real parts). Let $Q = q I_m \succ 0$, where $q > 0$, and $\Gamma = I_m$. where $L_s = \frac{L + L^\mathrm{T}}{2}$ is the symmetric part of the Laplacian matrix $L$, and $\d

Figures (12)

  • Figure 1: Illustration of a multi-network vehicular environment. The red icons represent private cars, green icons represent buses, and blue icons represent freight trucks. Multi-color vehicles simultaneously belong to different vehicular networks, serving as overlapping nodes with unique operational requirements and behaviors tailored to each network.
  • Figure 2: State trajectories of all vehicles converging to the desired state. Pinned vehicles are shown with solid red lines, unpinned vehicles with dashed blue lines, and the desired error is indicated by the black dashed line.
  • Figure 3: Statistical analysis of the number of pinned vehicles for control gains $c = 1, 2, 3, 4, 5$ in 50 trials of asymmetric coupling random scenarios, compared to the control gain selected by the LMI method.
  • Figure 4: Statistical analysis of pinning control consistency errors for control gains $c = 1, 2, 3, 4, 5$ in 50 trials of asymmetric coupling random scenarios, compared to the control gain selected by the LMI method.
  • Figure 5: Distribution of 50 Vehicles Across Three Networks and Selection Scheme for Pinning Vehicle Nodes
  • ...and 7 more figures

Theorems & Definitions (6)

  • Theorem 1
  • proof
  • Remark 1
  • Theorem 2
  • proof
  • Remark 2