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— This paper proposes a novel strategy using parallel optimization computations for nonlinear moving horizon state estimation, and parameter identification problems of dynamic systems. The parallelization is based on the multi-point derivative-based Gauss-Newton search, as one of the most efficient algorithms for the nonlinear least-square problems. A(More)
The estimation of parameters and states is one of the core data-processing algorithms used for the monitoring and control of continuum or structured mechatronical systems (e.g., flexible robotic arms and cantilevers). The measurements taken from the sensors combined with an appropriate model can filter the states and extract information about vibration(More)
An experimental comparison of two common parameter identification schemes is presented. The recursive least-squares method and the extended Kalman filter are applied to identify three parameters of a second-order linear mass-spring-damper model, using data obtained from a nanopositioning stage with a highly resonant dynamic response.
In this article the predictive control is suggested to control the injection fuel pulse width in such a manner that the air-fuel ratio deviates as little as possible from the stoichiometric ratio during the transients of the engine. The applied control strategy is based on the knowledge of an internal model of the air-path, predicting the change of the air(More)
The paper considers input observer based control of uncertain systems. A generalization of a simple input observer, that has been previously used in several automotive applications, is described and merged with a disturbance cancellation controller. The resulting control scheme enjoys semi-global practical closed-loop stability properties, i.e., under(More)
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