Output-feedback robust adaptive control for strict-feedback stochastic nonlinear systems under inverse optimality costs

The solvable problem of adaptive inverse optimal stabilization in probability is discussed, and control laws of global asymptotic stability in probability and adaptive inverse optimal stabilization in probability are developed for output-feedback stochastic nonlinear continuous systems with additive standard Wiener noises and constant unknown parameters by using Itô´s differentiation rule and adaptive backstepping algorithms. The adaptive control law and the parameter update laws can be obtained at same time by this design scheme.