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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