  We present a nonlinear model predictive control (MPC) scheme for tracking of
dynamic target signals. The scheme combines stabilization and dynamic
trajectory planning in one layer, thus ensuring constraint satisfaction
irrespective of changes in the dynamic target signal. For periodic target
signals we ensure exponential stability of the optimal reachable periodic
trajectory using suitable terminal ingredients and a convexity condition for
the underlying periodic optimal control problem. Furthermore, we introduce an
online optimization of the terminal set size to automate the trade-off between
fast convergence and operation close to the constraints. In addition, we show
how stabilization and dynamic trajectory planning can be formulated as
partially decoupled optimization problems, which reduces the computational
demand while ensuring recursive feasibility and convergence. The main tool to
enable the proposed design is a novel reference generic offline computation
that provides suitable terminal ingredients for tracking of dynamic reference
trajectories. The practicality of this approach is demonstrated on benchmark
examples, which demonstrates superior performance compared to state of the art
approaches.
