Periodic disturbance rejection of a class of nonlinear system

This study presents a periodic disturbance rejection method for a class of nonlinear systems with the input weighting vector in the proportional nonlinear form. Especially, the periodic disturbance does not match with the system input. A neural network approximator is employed for the estimation of the ideal feedforward control input that tackles the influence brought by disturbances in closed-loop system. Moreover, The adaptive control techniques are applied to deal with nonlinear uncertainties and unknown parameters in the system. The proposed control design ensures the closed-loop convergence of the system, i.e. all states converge to a small set around their equilibrium points. A simulation example is included to support this control approach.