Authors:
Flávio Luiz Rossini
1
;
Guilherme Santos Martins
2
;
João Paulo Silva Gonçalves
2
and
Mateus Giesbrecht
2
Affiliations:
1
Department of Electronic Engineering, Federal University of Technology - Paraná (UTFPR) Campo Mourão campus, Via Rosalina Maria dos Santos, 1233, Campo Mourão, PR and Brazil
;
2
Department of Semiconductors, Instruments and Photonics, School of Electrical and Computer Engineering (FEEC), University of Campinas (UNICAMP), Av. Albert Einstein, 400, Campinas, SP and Brazil
Keyword(s):
State-Variable Filter (SVF), Extended Kalman Filter (EKF), Recursive Least Squares State-variable Filter (RLSSVF) Method, Hybrid Algorithm.
Related
Ontology
Subjects/Areas/Topics:
Engineering Applications
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Nonlinear Signals and Systems
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
System Identification
;
System Modeling
Abstract:
In this paper, a method for the continuous time varying dynamical systems identification is presented. The study is based on the integration of the State-Variable Filter (SVF), the Extended Kalman Filter (EKF) and the Recursive Least Squares State-Variable Filter (RLSSVF). The main contribution of the algorithm applied in this paper is that a state space continuous time model can be estimated based on the system sampled inputs and outputs. To validate the method, a continuous time varying benchmark system is simulated and the benchmark parameters are compared to the estimated model parameters. The benchmark outputs are also compared to the model outputs to verify the accuracy of the proposed method. The results obtained show that the model reproduces the benchmark behavior accurately.