Adaptive neural tracking control for a class of non-lower triangular non-linear systems with dead zone and unmodelled dynamics

This study presents the disturbance observer-based adaptive neural tracking control approach for non-linear systems in non-strict-feedback form. The design difficulties including unmodelled dynamics and non-strict-feedback form are handled by resorting to a dynamic signal and the variable separation approach, respectively. A disturbance observer is constructed to cope with the effect of time varying disturbance. Neural networks are directly utilised to cope with the completely unknown non-linear functions and stochastic disturbances existing in systems. It is shown that the designed adaptive controller can guarantee that all the signals remain bounded and the desired signal can be tracked with a small domain of the origin. A numerical example is presented to illustrate the effectiveness of the proposed approach and an example of a real plant for one-link manipulator is provided to show the feasibility of the newly designed controller scheme.