  A distributed model predictive control scheme is developed for tracking
piecewise constant references where the terminal set is reconfigured online,
whereas the terminal controller is computed offline. Unlike many standard
existing schemes, this scheme yields large feasible regions without performing
offline centralized computations. Although the resulting optimal control
problem (OCP) is a semidefinite program (SDP), an SDP scalability method based
on diagonal dominance is used to approximate the derived SDP by a second-order
cone program. The OCPs of the proposed scheme and its approximation are
amenable to distributed optimization. Both schemes are evaluated using a power
network example and compared to a scheme where the terminal controller is
reconfigured online as well. It is found that fixing the terminal controller
results in better performance, noticeable reduction in computational cost and
similar feasible region compared to the case in which this controller is
reconfigured online.
