  Deriving precise system dynamic models through traditional numerical methods
is often a challenging endeavor. The performance of Model Predictive Control is
heavily contingent on the accuracy of the system dynamic model. Consequently,
this study employs Echo State Networks to acquire knowledge of the unmodeled
dynamic characteristics inherent in the system. This information is then
integrated with the nominal model, functioning as a form of model compensation.
The present paper introduces a control framework that combines ESN with MPC. By
perpetually assimilating the disparities between the nominal and real models,
control performance experiences augmentation. In a demonstrative example, a
second order dynamic system is subjected to simulation. The outcomes
conclusively evince that ESNbased MPC adeptly assimilates unmodeled dynamic
attributes, thereby elevating the system control proficiency.
