Definition
Many advanced controllers for nonlinear systems require knowledge of the model of the dynamics of the system to be controlled. The system dynamics is often called an “internal model,” and the resulting controller is model-based. If the model is not known, it can be learned with function approximation techniques. The learned model is subsequently used as if it were correct in order to synthesize a controller – the control literature calls this assumption the “certainty equivalence principle.”
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(2017). Internal Model Control. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_413
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_413
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