Adaptive neural-fuzzy and backstepping controller for port-Hamiltonian systems
Authors
Ahmad Taher Azar, Fernando E. Serrano, Marco A. Flores, Sundarapandian Vaidyanathan, Quanmin Zhu
Abstract
In this paper, a novel control strategy is shown for the stabilisation of dynamic systems in the form of port-Hamiltonian systems. This hybrid approach composed by a neural fuzzy and backstepping controller is implemented to stabilise the port-Hamiltonian system by dividing it into two blocks in order to separate the variables and yield an efficient control strategy. The proposed control strategy consists of a hybrid approach formed by a neural fuzzy and backstepping controller. The neural-fuzzy controller consists of two steps: an offline training implementing a gradient descent algorithm and an online training by a Lyapunov stability approach. The backstepping controller is designed by a recursive method considering the port- Hamiltonian system properties and implementing a Lyapunov stability approach. Along with the proposed control strategy, a neural-fuzzy observer is implemented to estimate the port- Hamiltonian system states considering the properties of the system representation. Finally, a cart pendulum example is shown to verify the effectiveness of the proposed observer and controller along with a comparative analysis.
Citation
- Journal: International Journal of Computer Applications in Technology
- Year: 2020
- Volume: 62
- Issue: 1
- Pages: 1
- Publisher: Inderscience Publishers
- DOI: 10.1504/ijcat.2020.103894
BibTeX
@article{Azar_2020,
title={{Adaptive neural-fuzzy and backstepping controller for port-Hamiltonian systems}},
volume={62},
ISSN={1741-5047},
DOI={10.1504/ijcat.2020.103894},
number={1},
journal={International Journal of Computer Applications in Technology},
publisher={Inderscience Publishers},
author={Azar, Ahmad Taher and Serrano, Fernando E. and Flores, Marco A. and Vaidyanathan, Sundarapandian and Zhu, Quanmin},
year={2020},
pages={1}
}