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

Enhancing Control Performance through ESN-Based Model Compensation in MPC for Dynamic Systems