Advancements in Distributed Control and Estimation

The field of control and estimation is moving towards more distributed and decentralized approaches, with a focus on scalability, robustness, and fault tolerance. Researchers are exploring new methods for state estimation, control, and coordination in complex systems, such as swarms of UAVs, multi-agent systems, and autonomous underwater vehicles. These advancements have the potential to enable more efficient, reliable, and adaptable systems in various applications, including search and rescue, surveillance, and delivery. Noteworthy papers in this area include:

  • Observer-Free Sliding Mode Control via Structured Decomposition, which proposes a novel control framework that eliminates the need for state observers and higher-order derivatives.
  • DMPC-Swarm, a distributed model predictive control methodology that integrates an efficient communication protocol with a novel DMPC algorithm to guarantee collision avoidance in swarms of nano UAVs.

Sources

Observer-Free Sliding Mode Control via Structured Decomposition: a Smooth and Bounded Control Framework

Swarming Without an Anchor (SWA): Robot Swarms Adapt Better to Localization Dropouts Then a Single Robot

Relative Navigation and Dynamic Target Tracking for Autonomous Underwater Proximity Operations

A Consensus Algorithm for Second-Order Systems Evolving on Lie Groups

Electromagnetic Formation Flying Using Alternating Magnetic Field Forces and Control Barrier Functions for State and Input Constraints

Distributed Safety-Critical MPC for Multi-Agent Formation Control and Obstacle Avoidance

Adaptive Control of Heterogeneous Platoons with Guaranteed Collision Avoidance

DMPC-Swarm: Distributed Model Predictive Control on Nano UAV swarms

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