High-gain Observer Based Robust Evolving Granular Feedback Linearization

ANDERSON BENTO; LUCAS OLIVEIRA; VALTER LEITE; IGNACIO RUBIO SCOLA; FERNANDO GOMIDE

Conferencia; XIV Conferência Brasileira de Dinâmica, Controle e Aplicações; 2019

Universidade de São Paulo

Feedback linearization is an important nonlinear model-based control technique, but in practice it performs poorly because of unavoidable modeling errors. Recently, a robust evolving granular feedback  linearization technique was introduced to handle modeling errors and improve control loop performance. The technique assumes that the system state is available for measurement what, in practice, is rarely possible. This paper develops an approach to further improve the effectiveness of the robust feedback linearization technique using a high-gain observer to estimate the system state. Simulation experiments with an angular position control system example show that the state estimation approach and feedback linearization effectivelly control the states to the desired references as specified by the designer.