Consensus Formation Tracking

Consensus formation tracking focuses on designing algorithms enabling multiple agents, whether physical robots or software entities, to agree on a common value or state, often while maintaining distributed control. Current research emphasizes robust algorithms, such as those based on distributed optimization, backstepping control with neurodynamic enhancements, and bio-inspired sliding mode control, to handle uncertainties and noise in real-world applications. These advancements are crucial for diverse fields, including multi-agent robotics, distributed machine learning (e.g., federated learning), and network systems, where secure and efficient consensus is essential for optimal performance and privacy. The development of privacy-preserving consensus algorithms is also a significant area of focus.