  We present a sequential distributed model predictive control (MPC) scheme for
cooperative control of multi-agent systems with dynamically decoupled
heterogeneous nonlinear agents subject to individual constraints. In the
scheme, we explore the idea of using tracking MPC with artificial references to
let agents coordinate their cooperation without external guidance. Each agent
combines a tracking MPC with artificial references, the latter penalized by a
suitable coupling cost. They solve an individual optimization problem for this
artificial reference and an input that tracks it, only communicating the former
to its neighbors in a communication graph. This puts the cooperative problem on
a different layer than the handling of the dynamics and constraints, loosening
the connection between the two. We provide sufficient conditions on the
formulation of the cooperative problem and the coupling cost for the
closed-loop system to asymptotically achieve it. Since the dynamics and the
cooperative problem are only loosely connected, classical results from
distributed optimization can be used to this end. We illustrate the scheme's
application to consensus and formation control.
