Identificación de parámetros en tiempo real de un motor de CD de imanes permanentes sin escobillas

Abstract

This thesis addresses the problem of identifying the dynamic model parameters of a permanent magnet brushless DC motor: stator resistance and inductance, back-emf force, damping coefficient and moment of inertia. The parameter identification is done through real-time measurements of the electrical and mechanical variables of a 6000 rpm, motor, and through the analysis of transient and steady-state responses in different operation regimes. Standard tests, such as no-load with the machine operating as a motor or a generator, blocked rotor and resistance measurements were adapted to fit the operation characteristics of this kind of motors. Startup and open-loop schemes were proposed, as well as an algorithm for running with only two position sensors. The recursive least-squares algorithm with forgetting factor was used to identify the damping coefficient and moment of inertia of the first order approximation of the motor. The results are validated against manufacturer provided information and by comparing the experimental and simulation results using the state space model in open and closed-loop operation.

Modeling and Control of a DC-DC Buck–Boost Converter with Non-Linear Power Inductor Operating in Saturation Region Considering Electrical Losses

“…The recursive least-square identification algorithm is used to identify the dynamics of discrete processes [23]. This algorithm was used in previous studies to fit transfer functions in the z-domain considering a set of sampled data as in [24]. Consider the strictly proper discrete transfer function as the following expression:…”