Abstract
Rates of convergence are derived for approximate solutions to optimization problems associated with the design of state estimators for nonlinear dynamic systems. Such problems consist in minimizing the functional given by the worst-case ratio between the ℒ p -norm of the estimation error and the sum of the ℒ p -norms of the disturbances acting on the dynamic system. The state estimator depends on an innovation function, which is searched for as a minimizer of the functional over a subset of a suitably-defined functional space. In general, no closed-form solutions are available for these optimization problems. Following the approach proposed in (Optim. Theory Appl. 134:445–466, 2007), suboptimal solutions are searched for over linear combinations of basis functions containing some parameters to be optimized. The accuracies of such suboptimal solutions are estimated in terms of the number of basis functions. The estimates hold for families of approximators used in applications, such as splines of suitable orders.
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Communicated by V.F. Demyanov.
G. Gnecco and M. Sanguineti were partially supported by the project Ateneo 2008 “Solution of functional optimization problems by nonlinear approximators and learning from data” of the University of Genoa.
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Alessandri, A., Gnecco, G. & Sanguineti, M. Minimizing Sequences for a Family of Functional Optimal Estimation Problems. J Optim Theory Appl 147, 243–262 (2010). https://doi.org/10.1007/s10957-010-9720-3
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DOI: https://doi.org/10.1007/s10957-010-9720-3