Authors: Ahmad Taher Azar; Fernando E. Serrano; Marco A. Flores; Sundarapandian Vaidyanathan; Quanmin Zhu
Addresses: Robotics and Internet-of-Things Lab (RIOTU), Prince Sultan University, Riyadh, Saudi Arabia; Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt ' Central American Technical University UNITEC, Zona Jacaleapa, Tegucigalpa, Honduras ' Energy Division, Physics Department, Honduras National Autonomous University (UNAH), Tegucigalpa, Honduras ' Research and Development Centre, Vel Tech University, Chennai, Tamil Nadu, India ' Department of Engineering Design and Mathematics, University of the West of England, Bristol, UK
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.
Keywords: neural-fuzzy system; backstepping control; observer design; port-Hamiltonian systems.
International Journal of Computer Applications in Technology, 2020 Vol.62 No.1, pp.1 - 12
Received: 07 Nov 2018
Accepted: 28 Jan 2019
Published online: 28 Nov 2019 *