Adaptive-gain observer-based stabilization of stochastic strict-feedback systems with sensor uncertainty. (October 2020) - Li, Wuquan; Yao, Xiaoxiao; Krstic, Miroslav
Abstract: We study the adaptive output-feedback stabilization problem of stochastic strict-feedback systems with sensor uncertainty. Specifically, we consider the simultaneous presence of sensor uncertainty, unknown growth rate and stochastic disturbance, which has not been treated heretofore. By developing a new stochastic adaptive dual-domination approach, an adaptive observer and an output-feedback controller are designed, in which two gains are suitably selected to dominate the unknown sensor sensitivity and unknown growth rate, respectively. By using the nonnegative semimartingale convergence theorem, it is proved that the closed-loop system has an almost surely unique solution on [ 0, + ∞ ) and that regulation to the equilibrium at the origin of the closed-loop system is achieved almost surely. Finally, two simulation examples are given to illustrate the control design.