Coordinated tracking control of multi agent systems with full-state constraints

Abstract

This paper presents a solution of the distributed coordinated tracking for multiagents modeled by Euler-Lagrange systems (ELSs), where model uncertainties and full-state constraints (FSCs) are considered. We first introduce a distributed estimator to obtain accurate estimations of the target trajectory. To guarantee the FSCs of ELSs, we next employ the barrier Lyapunov function to design a distributed coordinated tracking law combined with Moore-Penrose inverse. In order to handle the model uncertainties, the adaptive neural networks are then integrated into the proposed control law. Through the application of the Lyapunov stability theory, state errors are guaranteed that semi-global uniform ultimate boundedness. Simulation results finally demonstrate the feasibility of the developed strategies.