Adaptação de segundo nível como técnica de estimação de parâmetros e sua aplicação ao controle adaptativo por modelo de referência

Abstract

In this dissertation we demonstrate the concept of second level adaptation as a parameter estimation method based on multiple linear regression identification models for the case of a plant of order unity, and the case of a plant of order n with single input and output available for measurement (SISO). We propose a modified form of the adaptive law for second level adaptation based on integration of transient information. In all cases simulation studies show that the estimates reach their true values faster with second level adaptation compared to individual identification models and that the proposed modification is even faster and also smoother in this regard. We apply second level adaptation based on linear regression identification models updated through the gradient method to the problem of model reference adaptive control (MRAC) in the case of an order 1 plant and the case of an order n and relative degree one SISO plant, in this case with normalized gradient method. Simulation results show that the control signal generated with second level adaptation yields better results of model reference tracking compared to individual identification models. We also compare the indirect MRAC based on second level adaptation to the variable structure model reference adaptive control (VS-MRAC) scheme.