Adaptive neural control for high order Markovian jump nonlinear systems with unmodeled dynamics and dead zone inputs

This paper focuses on the adaptive control design for a class of high order Markovian jump nonlinear systems with unmodeled dynamics and unknown dead-zone inputs. The unknown parameter vector, the dynamic uncertainties, the unknown nonlinear functions and the actuator dead-zone nonlinearities are all allowed to be randomly varying with the Markovian modes. By introducing the bound estimation approach, the effect of randomly jumping unknown parameters and the varying dead-zone nonlinearities are tackled. Moreover, aiming at the unmodeled dynamics and completely unknown nonlinear functions which have Markovian jumping features, several two-layer neural networks (NNs) are introduced for each mode and the adaptive backstepping control law is finally established. The stochastic stability analysis for the closed-loop system are also performed. At last, a numerical example is provided to illustrate the efficiency and advantages of the proposed method.