State observer-based adaptive neural dynamic surface control for a class of uncertain nonlinear systems with input saturation using disturbance observer

This paper focuses on the problem of tracking control aiming at a class of uncertain nonlinear systems in the presence of input saturation, unknown external disturbances and unmeasured states on the basis of adaptive neural dynamic surface control (DSC) scheme in combination with state and disturbance observers. The unknown nonlinear system functions are approximated by Radial Basis Function Neural Networks, the unmeasured states are estimated by a developed state observer and the unknown compounded disturbances are estimated by nonlinear disturbance observers. In addition, by introducing DSC technique, the problem of “explosion of complexity” inherent in the conventional backstepping method is eliminated. The designed controller ensures the semi-global stabilization of the whole closed-loop system by means of Lyapunov analysis method. The effectiveness of the proposed approach is demonstrated through a numerical example.