Output feedback neural network adaptive tracking control for pure-feedback nonlinear systems

Output feedback tracking control scheme is investigated for a class of pure-feedback nonlinear systems in this paper. The method shows that the pure-feedback systems can be transformed into the standard non-affine form to avoid backstepping design. The observer gain and controller are simultaneously tuned according to output tracking error based on non-separation principle design. The distinguished aspect of the proposed algorithm is that no Lipschitz assumption and SPR condition are employed which makes the system construct simple. Output tracking error and all states in the closedloop system are guaranteed to be semi-globally ultimately bounded by Lyapunov approach. Finally the simulation results demonstrate the effectiveness of the control scheme.