Control effort reduction in output-feedback stabilization for a class of stochastic nonlinear systems

This paper considers a class of stochastic nonlinear systems with linearly bounded unmeasurable states from the viewpoint of reducing the control effort. Our main work consists of the following aspects: (1) By choosing an output-feedback controller with an improved algorithm of choosing critical value for gain parameter, we show that the critical value for gain parameter of observer is smaller than that obtained by the existing work without imposing any additional assumption. Thus, less control effort and rate of change of the controller are required. (2) The designed output-feedback controller maintains that the closed-loop system is bounded in probability, the equilibrium at the origin is globally asymptotically stable (GAS) in probability. (3) The problem of inverse optimal stabilization in probability is also solved. Simulation example demonstrates the effectiveness of the control scheme.