A High-Order Sliding-Mode Adaptive Observer for Uncertain Nonlinear Systems

A high-order sliding-mode adaptive observer is proposed to solve the problem of an adaptive estimation, i.e., the simultaneous estimation of the state and parameters, for a class of uncertain nonlinear systems in the presence of external disturbances, which do not need to satisfy a relative degree condition equal to one. This approach is based on a high-order sliding-mode observer and a nonlinear parameter identification algorithm. The practical, global, and uniform asymptotic stability of the adaptive estimation error, despite the external disturbances, is guaranteed through the small-gain theorem. The convergence proofs are developed based on the Lyapunov and input-to-state stability theories. Some simulation results illustrate the performance of the proposed high-order sliding-mode adaptive observer.

Automated summary

In this paper, a high-order sliding-mode adaptive observer is proposed for the adaptive estimation of a class of uncertain nonlinear systems in the presence of external disturbances. The proposed approach does not require the satisfaction of a relative degree condition equal to one. The convergence of the adaptive estimation error, despite the external disturbances, is guaranteed through the small-gain theorem. The performance of the proposed high-order sliding-mode adaptive observer is demonstrated through simulation results.