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This paper addresses the design of an optimization-based cooperativepath-following control law for multiple robotic vehicles that optimallybalances the transient trade-off between coordination and path-followingerrors. To this end, we formulate a more general multi-agent framework whereeach agent is associated with (i) a continuous-time dynamical model, whichgoverns the evolution of its state, and (ii) an output equation that is afunction of both the state of the agent and a coordination vector. According toa given network topology, each agent can access its state and coordinationvector, as well as the coordination vectors of the neighboring agents. In thissetup, the goal is to design a distributed control law that steers the outputsignals to the origin, while simultaneously driving the coordination vectors ofthe agents of the network to consensus. To solve this, we propose a modelpredictive control scheme that builds on a pre-existing auxiliary consensuscontrol law to design a performance index that combines the output regulationobjective with the consensus objective. Convergence guarantees under which onecan solve this coordinated output regulation problem are provided. Numericalsimulations display the effectiveness of the proposed scheme applied to acooperative path following control problem of a network of 3D nonholonomicrobotic vehicles.

An optimization-based cooperative path-following framework for multiple robotic vehicles