Adaptive Fuzzy Risk-Sensitive Control for Stochastic Strict-Feedback Nonlinear Systems With Unknown Uncertainties

This article investigates the adaptive fuzzy control problem for a class of stochastic nonlinear systems with the risk-sensitive performance index. The desired cost level of the risk-sensitive index, which could be arbitrarily small, is guaranteed by the solution of a specified Hamilton–Jacobi–Bellman (HJB) equation. By considering the unknown uncertainties of the stochastic nonlinear systems, a novel adaptive fuzzy risk-sensitive control method is proposed, which guarantees the input-to-state stability of the system. In addition, the proposed control strategy also reduces the conservatism of the existing adaptive robust control method. Finally, the effectiveness of the proposed approach is verified by a simulation example of one-link manipulator.