An Optimization-Based Cooperative Path-Following Framework for Multiple Robotic Vehicles

• Tipo: Artigo em Revista Científica Internacional
• Data: 2020

Autores

Alessandretti, A (Autor) Outra; Aguiar, AP (Autor) FEUP

Revista

Título: IEEE Transactions on Control of Network Systems

Vol. 7

Páginas: 1002-1014

ISSN: 2325-5870

Editora: IEEE

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00R-YQC

DOI: 10.1109/tcns.2019.2953459

Abstract (EN): This article addresses the design of an optimization-based cooperative path-following control law for multiple robotic vehicles that optimally balances the transient tradeoff between coordination and path-following errors. To this end, we formulate a more general multi-agent framework where each agent is associated with a continuous-time dynamical model, which governs the evolution of its state, and an output equation that is a function of both the state of the agent and a coordination vector. According to a given network topology, each agent can access its state and coordination vector, as well as the coordination vectors of the neighboring agents. In this setup, the goal is to design a distributed control law that steers the output signals to the origin, while simultaneously driving the coordination vectors of the agents of the network to consensus. To solve this, we propose a model predictive control scheme that builds on a pre-existing auxiliary consensus control law to design a performance index that combines the output regulation objective with the consensus objective. Convergence guarantees under which one can solve this coordinated output regulation problem are provided. Numerical simulations display the effectiveness of the proposed scheme applied to a cooperative path following the control problem of a network of 3-D nonholonomic robotic vehicles.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 13