Control of complex systems with generalized embedding and empirical dynamic modeling.

ABSTRACT: 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

SUBMITTER: Park J 

PROVIDER: S-EPMC11293753 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature