The control design method for a class of non-strict feedback nonlinear systems is studied in this brief considering uncertain nonlinearities and unknown non-symmetrical input dead-zone. Combining with the finite-time command filtered backstepping (FCFB) technique, a novel finite-time adaptive control approach is proposed in which a neural network-based methodology is adopted to cope with the uncertain nonlinearities in the non-strict feedback form. The input dead-zone model is transformed into a simple linear system with unknown gain and bounded disturbance which is estimated by an adaptive factor. Using the finite-time Lyapunov theory, the system convergence is proved. And the effectiveness of the proposed control scheme is verified through comparative numerical simulations.

This paper presents an adaptive fast finite-time consensus (FTC) for second-order uncertain nonlinear (UN) multi-agent systems (MASs) with unknown nonsymmetric dead-zone and external disturbances. In the process of control protocols design, based on the estimation of the dead-zone width that is obtained by using adaptive method, a fuzzy logic dead-zone compensator is adopted to deal with the nonsymmetric dead-zone input phenomenon. Combining finite-time control technique and Lyapunov’s relevant theory, a new fast FTC protocol is developed. Based on the basis of radial basis function neural networks (RBFNNs) theories, the unknown nonlinear functions are approximated. Under the presented consensus protocols and adaptive laws, it can be proved that the position errors of arbitrary two agents can reach a small region of zero in finite time as well as the velocity errors. Ultimately, the effectiveness of the designed method is tested via two numerical examples.