Panel discussion @ virtual NMPC 2021

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. However, this evident and continued success renders it increasingly complex to live up to industrial expectations and enabling graduate students for state-of-the-art research in MPC. Clearly, key to solving this issues is a sustainable and modern way of teaching MPC. In this context, the panel discussed promising approaches such as early courses on MPC within the bachelor programm. Different perspectives were contributed by the five panelists Jim Rawlings (UC Santa Barbara), Eric Kerrigan (Imperial College London), Alessandra Parisio (U Manchester), Maria Prandini (Politecnico di Milano), and Filip Logist (BASF Antwerpen). The discussion was moderated by Timm Faulwasser (TU Dortmund) and Martin Mönnigmann (Ruhr-University Bochum). Some hypothesis that formed the basis for the debate can be found in our paper:

T. Faulwasser, S. Lucia, M. Schulze Darup, and M. Mönnigmann, Teaching MPC: Which Way to the Promised Land?, in Proc. of the 7th IFAC Conference on Nonlinear Model Predictive Control (NMPC), pp. 240-245, 2021. Preprint: arXiv:2106.00944

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