  Since the earliest conceptualizations by Lee and Markus, and Propoi in the
1960s, Model Predictive Control (MPC) has become a major success story of
systems and control with respect to industrial impact and with respect to
continued and wide-spread research interest. The field has evolved from
conceptually simple linear-quadratic (convex) settings in discrete and
continuous time to nonlinear and distributed settings including hybrid,
stochastic, and infinite-dimensional systems. Put differently, essentially the
entire spectrum of dynamic systems can be considered in the MPC framework with
respect to both -- system theoretic analysis and tailored numerics. Moreover,
recent developments in machine learning also leverage MPC concepts and
learning-based and data-driven MPC have become highly active research areas.
  However, this evident and continued success renders it increasingly complex
to live up to industrial expectations while enabling graduate students for
state-of-the-art research in teaching MPC. Hence, this position paper attempts
to trigger a discussion on teaching MPC. To lay the basis for a fruitful
debate, we subsequently investigate the prospect of covering MPC in
undergraduate courses; we comment on teaching textbooks; and we discuss the
increasing complexity of research-oriented graduate teaching of~MPC.
