A sampled-data model predictive framework for cooperative path following of multiple robotic vehicles

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

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

Ata de Conferência Internacional

Título: Lecture Notes in Control and Information Sciences

Páginas: 473-494

Workshop on Sensing and Control for Autonomous Vehicles: Applications to Land, Water and Air Vehicles, 2017

20 June 2017 through 22 June 2017

Indexação

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-00N-9RG

DOI: 10.1007/978-3-319-55372-6_22

Abstract (EN): We present a sampled-data model predictive control (MPC) framework for cooperative path following (CPF) of multiple, possibly heterogeneous, autonomous robotic vehicles. Under this framework, input and output constraints as well as meaningful optimization-based performance trade-offs can be conveniently addressed. Conditions under which the MPC-CPF problem can be solved with convergence guarantees are provided. An example illustrates the proposed approach. © Springer International Publishing AG 2017.

Idioma: Inglês

Tipo (Avaliação Docente): Científica