Adaptive neural network tracking control of multi-agent systems with state constraints
Dongyu Li, Guangfu Ma, Chuangjiang Li, Wei Zhang, Wei He · 2017
This paper studies the distributed coordinated tracking problem for multiple Euler-Lagrange systems (MELSs) with full-state constraints. Firstly, a distributed finite-time sliding-mode estimator (DFSE) is introduced to access precise estimations of the leader's position and velocity. Then, to guarantee the full-state constraints of MELSs, we use the barrier Lyapunov function (BLF) technique and a Moore-Penrose inverse term to design a distributed coordinated tracking control law. The asymptotic convergence of all the state errors can be proofed by Lyapunov stability analysis. Finally, simulation results are given to illustrate the feasibility of the proposed control law.