Direct Neural Network Adaptive Tracking Control for Uncertain Non-Strict Feedback Systems With Nonsymmetric Dead-Zone

In this paper, combined with the approximation of neural network, a novel direct adaptive alleviating tracking control algorithm is presented for a class of non-strict feedback uncertain nonlinear systems. Here, both nonlinear uncertainties and nonsymmetric dead-zone inputs are considered. First, according to some coordinate transforms and variable separation methods, the non-strict feedback form is converted into the normal form. Second, the relationship of state vector and error functions are established, and the inputs of dead-zone are compensated with adaptive approaches. This novel direct scheme assumes that the approximation error and optimal approximation norms of NN are to be bounded by unknown constants and can alleviate the number of online adjusted parameters so as to improve the robust control performance of the systems. At last, under Lyapunov theorem analysis, the uniformly ultimately boundness of all the signals in the closed-loop systems can be guaranteed and the dead-zone inputs can be compensated, the effectiveness of this algorithm is well demonstrated by simulation results.