Practical consensus tracking control for networked Euler–Lagrange systems based on UDE integrated with RBF neural network

Abstract

This paper solves the practical consensus tracking control problem for networked Euler–Lagrange (EL) systems using the uncertainty and disturbance estimator (UDE) in combination with the radial basis function (RBF) neural network. An integrated consensus algorithm is proposed for the networked EL systems, where the RBF neural network is first utilized to generate online estimation of the uncertain EL dynamics for enhancing the system adaptability, and then the UDE is applied to compensate external disturbances dynamically for improving the system robustness. Furthermore, a unified analysis framework for the closed-loop control system is established through performing the proper filter design and Lyapunov-like analysis. The proposed consensus tracking algorithm possesses a simple system structure for implementation and has a good robust performance in dealing with both uncertainties and disturbances. Numerical simulations are used to verify the effectiveness of the developed integrated consensus scheme.