Adaptive neural-fuzzy and backstepping controller for port-Hamiltonian systems

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.