Linear vs. Nonlinear Modeling of Continuum Robotic Arms Using Data-Driven Method
| Autor: | Aida Parvaresh, S. Ali A. Moosavian |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Nonlinear autoregressive exogenous model Inverse kinematics Computer science Underactuation 02 engineering and technology Data modeling Nonlinear system 020901 industrial engineering & automation Autoregressive model Control theory 0202 electrical engineering electronic engineering information engineering Trajectory 020201 artificial intelligence & image processing Robotic arm |
| Zdroj: | 2019 7th International Conference on Robotics and Mechatronics (ICRoM). |
| DOI: | 10.1109/icrom48714.2019.9071914 |
| Popis: | Dynamics modeling of continuum robotic arms is of great importance due to the highly nonlinear characteristics, uncertain and complex structure, and the inherent underactuation. This affects further usage in various aspects, including inverse kinematics, trajectory generation, control and optimization. In this paper, a modelling approach is proposed through the use of data-driven identification by linear and nonlinear models known as ARX (autoregressive with exogenous terms) and NARX (nonlinear autoregressive with exogenous terms) models. The unknown parameters in the ARX model are the system parameters; while the structure is known. However, for NARX model, the whole structure is considered to be unknown. These two structures are used to model a single-section continuum robotic arm, and the results are compared. Finally, the advantages and disadvantages of them are discussed. |
| Databáze: | OpenAIRE |
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