Adaptive neural network control of uncertain minimum-phase nonlinear systems

The paper considers the problem of practical disturbance attenuation for a class of minimum-phase uncertain nonlinear systems. The system under consideration is cascaded by a zero-input asymptotically stable nonlinear sub-system and a triangle cascade one with some uncertainties and disturbance. For the purpose of reducing the reservation which appears in robust control method, by using the backstepping technique, we construct a neural network control method. Under the controller, the resultant closed loop system is input to state practically stable and the response of the output to the disturbance is practically attenuated. The simulation example shows that the controller has a small control gain and the resultant closed-loop system has good performance.