A Sampled-data Model Predictive Framework for Cooperative Path Following of Multiple Robotic Vehicles

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Aguiar, AP; Rucco, A; Alessandretti, A

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

ID Authenticus: P-00N-9RG

DOI: 10.1007/978-3-319-55372-6_22

Abstract: 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.

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