Inverse optimal adaptive stochastic gain assignment for a class of nonlinear systems with Markovian jump

This paper deals with the inverse optimal adaptive stochastic gain assignment problem for a class of Markovian jump nonlinear systems with constant unknown parameters. The Wiener noises have bounded but unknown covariance. It is shown that a sufficient condition to solve this problem is the existence of a Lyapunov function for a corresponding auxiliary system. By employing backstepping technique and common Lyapunov function method, an adaptive control law is designed, which solves this problem for a class of Markovian jump nonlinear systems in strict-feedback form. A numerical example is given to illustrate the theoretical analysis result.