A Lecture on Model Predictive Control - Carnegie Mellon …

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– State Estimation • Lack of sensors for key variables – Reducing computational complexity • approximate solutions, preferably with some guaranteed properties – Better management of “uncertainty” • creating models with uncertainty information (e.g., stochastic model) • on-line estimation of parameters / states

  Model, Control, Estimation, Stochastic