Multi-Agent Consensus Algorithms

Research on Consensus Algorithms in Multi-Agent Systems

In multi-agent systems, consensus algorithms serve as the core technology for achieving cooperative control. Based on different time models, they are primarily categorized into continuous-time consensus algorithms and discrete-time consensus algorithms.

Continuous-time consensus algorithms typically employ differential equations for modeling, where appropriate control protocols are designed to ensure asymptotic convergence of all agents' states to a common value in continuous time. These algorithms find extensive applications in scenarios like UAV formation control and distributed sensor networks. From an implementation perspective, such algorithms often utilize gradient-based update rules and Lyapunov stability analysis to guarantee convergence.

Discrete-time consensus algorithms adopt difference equations to describe system dynamics, achieving consensus through iterative updates. Compared to continuous-time versions, discrete algorithms are more suitable for digital system implementation and better handle communication delays and sampling issues. Code implementation typically involves matrix-based state updates where each agent's new state is computed as a weighted average of neighbors' previous states, often implemented using adjacency matrices or Laplacian matrices in programming frameworks.

Both algorithm types share the core concept of utilizing local information exchange among neighboring nodes to achieve global consensus through distributed computation. Key design considerations include convergence speed analysis, communication topology optimization (using graph theory concepts), and robustness enhancement against network uncertainties. Implementation often involves designing update rules with convergence guarantees, where critical functions may include neighbor state sampling, weight calculation, and convergence validation checks.