Adaptive H∞ formation control for Euler-Lagrange systems by utilizing neural network approximators

Design methods of adaptive H
_∞
formation control of multi-agent systems composed of Euler-Lagrange systems by utilizing neural network approximators are presented in this paper. The proposed control schemes are derived as solutions of certain H
_∞
control problems, where estimation errors of tuning parameters, error terms in potential functions, and approximate and algorithmic errors in neural network estimation schemes are regarded as external disturbances to the process. It is shown that the resulting control systems are robust to uncertain system parameters and that the desirable formations are achieved asymptotically via adaptation schemes.