  Effective control requires knowledge of the process dynamics to guide the
system toward desired states. In many control applications this knowledge is
expressed mathematically or through data-driven models, however, as complexity
grows obtaining a satisfactory mathematical representation is increasingly
difficult. Further, many data-driven approaches consist of abstract internal
representations that may have no obvious connection to the underlying dynamics
and control, or, require extensive model design and training. Here, we remove
these constraints by demonstrating model predictive control from generalized
state space embedding of the process dynamics providing a data-driven,
explainable method for control of nonlinear, complex systems. Generalized
embedding and model predictive control are demonstrated on nonlinear dynamics
generated by an agent based model of 1200 interacting agents. The method is
generally applicable to any type of controller and dynamic system representable
in a state space.
