This paper investigates the distributed adaptive neural consensus tracking control for multiple Euler-Lagrange systems with\nparameter uncertainties and unknown control directions. Motivated by the Nussbaum-type function and command-filtered\nbackstepping technique, the error compensations and neural network approximation-based adaptive laws are established, which\ncan not only overcome the computation complexity problem of backstepping but also make the consensus tracking errors reach to\nthe desired region although the control directions and system nonlinear dynamics are both unknown. Numerical example is given\nto show the proposed algorithm is effective at last.
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