Distributed finite-time tracking control for multiple uncertain Euler–Lagrange systems with input saturations and error constraints

This study addresses the distributed finite-time tracking control for multiple uncertain Euler–Lagrange systems with input saturations and error constraints. In order to achieve distributed finite-time tracking control in the model-independent way, neural networks are used to approximate the system unknown terms. Besides, the authors consider that there exists input saturation for each dimension of each follower in the systems. The input saturations are tackled by converting the systems with input saturations to the cases with unknown input gains. Then, based on backstepping technique, the distributed finite-time tracking control law is developed. Tan-type barrier Lyapunov function is utilised to guarantee that the error variables would not exceed the predefined bounds. Lyapunov theory and graph theory demonstrate the finite-time stability of the systems. By choosing a class of two-link manipulators consisting of one leader and four followers, numerical simulations are provided to show the effectiveness of the proposed control algorithm.