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A three-stage computation framework for solving parameter estimation problems for dynamic systems with multiple data profiles is developed. The dynamic parameter estimation problem is transformed into a nonlinear programming (NLP) problem by using collocation on finite elements. The model parameters to be estimated are treated in the upper stage by solving an NLP problem. The middle stage consists...
Many real life problems can be modeled as nonlinear global optimization problems. Such problems often have multiple local minima and thus require global optimization methods. One of the more recent global optimization tools is known as the filled function method which is a promising way used in unconstrained global optimization. Several filled functions with one or two parameters have already been...
We propose a novel control algorithm, probabilistically constrained predictive control, to deal with the uncertainties of system disturbances. The output is to be controlled in the constrained range with a desired probability. Under the assumption of a linear system, the formulated joint probabilistically constrained problem is convex. Thus, it can be solved with a nonlinear programming solver. The...
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