Authors: Anderson Bento; Lucas Oliveira; Valter Leite; Ignacio Rubio Scola; Fernando Gomide.
Title: High-gain Observer Based Robust Evolving Granular Feedback Linearization.
Resumen: 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.
Meeting type: Conferencia.
Type of job: Artículo Completo.
Production: High-gain Observer Based Robust Evolving Granular Feedback Linearization.
Scientific meeting: XIV Conferência Brasileira de Dinâmica, Controle e Aplicações.
Organizing Institution: Universidade de São Paulo.
It's published?: Yes
Publication place: São Carlos
Meeting month: 11