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An on line optimal control approach based on moving horizon strategy is developed.
The moving horizon estimation and control (also called predictive control) technology is applied and simulated.
With the characteristics of moving horizon this algorithm can be applied to online-optimization under uncertainty.
A moving horizon estimation (MHE) approach to simultaneously estimate states and parameters is revisited.
Within the confidence regions, the moving horizon estimation scheme is allowed to optimize its estimates.
The local estimators are designed as observer-enhanced moving horizon estimators.
Subsequently, a local moving horizon estimation (MHE) scheme is designed for each subsystem.
It uses a least squares estimation implemented on the lines of the moving horizon estimation.
For the dynamic automatic adjustment to changing kinetics, a moving horizon estimator (MHE) is applied.
A moving horizon approach is proposed, where each sensor has to solve a quadratic programming problem at each instant.
Finally, a Moving Horizon estimator is implemented and simulation experiments are conducted to verify the accuracy of the estimator.
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