Abstract
The objective of this study is to design a robust receding-horizon observer for systems described by nonlinear models with uncertain parameters. Robustification in the presence of model uncertainties naturally leads to the formulation of a nonlinear min-max optimization problem, which can either be solved numerically or which can be converted to a simpler minimization problem using linearization along a nominal trajectory and recent results in linear robust receding-horizon estimation. This method is first evaluated in simulation and then with real-life experimental data collected from continuous cultures of phytoplankton.
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