  This paper presents a distributed model predictive control (DMPC) scheme for
nonlinear continuous-time systems. The underlying distributed optimal control
problem is cooperatively solved in parallel via a sensitivity-based algorithm.
The algorithm is fully distributed in the sense that only one
neighbor-to-neighbor communication step per iteration is necessary and that all
computations are performed locally. Sufficient conditions are derived for the
algorithm to converge towards the central solution. Based on this result,
stability is shown for the suboptimal DMPC scheme under inexact minimization
with the sensitivity-based algorithm and verified with numerical simulations.
In particular, stability can be guaranteed with either a suitable stopping
criterion or a fixed number of algorithm iterations in each MPC sampling step
which allows for a real-time capable implementation.
