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

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Aguiar, AP (Author) FEUP; Rucco, A (Author) Other; Alessandretti, A (Author) Other

Conference proceedings International

Title: Lecture Notes in Control and Information Sciences

Pages: 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

Indexing

Scopus - 3 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific