A Passivity-Agnostic Framework for Distributed Adaptive Synchronization under Unknown Leader Dynamics
New control framework guarantees synchronization for heterogeneous robot swarms without requiring strict passivity conditions.
Researchers Moh Kamalul Wafi and Milad Siami have introduced a novel control framework for synchronizing networks of heterogeneous agents, such as drone swarms or robotic teams, that operates without requiring the system to be strictly positive real (SPR) beforehand. Published and accepted for the 2026 American Control Conference, this 'passivity-agnostic' design is a significant advancement because it handles agents with unknown and potentially unstable internal dynamics while relying only on position data shared between neighbors, not full state information. The framework is robust against bounded external disturbances, a common real-world challenge.
The technical innovation lies in its dual-mode operation. If the closed-loop system is SPR, a structured reparameterization yields gradient-based adaptive error dynamics, proven stable via Lyapunov analysis. If the system is not SPR, the framework employs frequency shaping to recover an 'effective passivity' property, enabling the same stability guarantees using the Meyer-Kalman-Yakubovich lemma. Simulations demonstrated that the performance of frequency-shaped non-SPR designs matched the ideal SPR case across various network topologies, enabling robust tracking and parameter adaptation under multiple disturbance profiles. This removes a major design constraint for engineers building reliable multi-agent systems.
- Framework operates without requiring strict positive realness (SPR) a priori, certifying it when present and recovering it via frequency shaping when absent.
- Guarantees global asymptotic synchronization for heterogeneous second-order agents using only position communication and handles unknown, potentially unstable leader dynamics.
- Provides exact rejection of constant disturbances and bounded responses to time-varying disturbances, with parameter convergence under persistent excitation.
Why It Matters
Enables more reliable and easier-to-design swarms of drones, robots, or autonomous vehicles that must coordinate in uncertain, real-world environments.