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In this paper one presents a model and an analysis of a dc-to-dc Buck converter, in closed loop, from 24V to 12 V with a power of 500 W. Model is implemented in MATLAB/SIMULINK toolbox SIMPOWERSYS. Control system is a fuzzy logic one, Mamdani type, and using P.I. configuration. One opted for a fuzzy controller due to its flexibility in its configuration and tuning process, and also because it doesn’t need to know a mathematical model for the Buck converter. Fuzzy controller is composed of two sub-controllers in parallel, one for the proportional component and a second for integrative component. One proposes this configuration because fewer inference rules are necessary and also the tuning process is easily accomplished compared to a fuzzy controller with two inputs. Implemented model is validated by numerical simulations and the results are realistic and confirm the possibility to use this type of controller. [ABSTRACT FROM AUTHOR]

The paper presents a way to obtain an intelligent miniaturized three-axial accelerometric sensor, based on the on-line estimation and compensation of the sensor errors generated by the environmental temperature variation. Taking into account that this error's value is a strongly nonlinear complex function of the values of environmental temperature and of the acceleration exciting the sensor, its correction may not be done off-line and it requires the presence of an additional temperature sensor. The proposed identification methodology for the error model is based on a neuro-fuzzy procedure which process off-line the numerical values obtained from the accelerometer experimental testing for different values of acceleration applied to its axes of sensitivity and for different values of operating temperature. In the sections of the paper are shown the results of the experimental testing of the accelerometer on all the three sensitivity axes, the identification of the error models on each axis by using fuzzy logic techniques combined with adaptive neural-network techniques, and the validation of the obtained models with experimental values. After the training of the neuro-fuzzy developed models for all of the three detection channels was obtained a reduction by almost two orders of magnitude of the acceleration absolute maximum error due to environmental temperature variation. [ABSTRACT FROM AUTHOR]