Robust neuro-adaptive cooperative control of multi-agent port-controlled Hamiltonian systems

This paper presents the distributed cooperative tracking control of the multi-agent port-controlled Hamiltonian (PCH) systems that are networked through a directed graph. Controller is made robust against the parametric uncertainties using neural networks. Dynamics of the the proposed novel neural network tuning law is driven by both the position and the velocity errors owing to the information preserving filtering of the Hamiltonian gradient. In addition, the PCH structure of the closed-loop system is preserved and the controller achieves the 葦₂ disturbance attenuation objective. Simulations are performed on a group of robotic manipulators to demonstrate the efficacy of the proposed controller.