Discrete-time sliding mode neural observer for continuous time mechanical systems

This paper proposes a novel discrete-time velocity observer which uses neural network and sliding mode for unknown continuous time mechanical systems. The neural observer in this paper has two stages: first a dead-zone neural observer assures that the observer error is bounded, then super-twisting second-order sliding-mode is used to guarantee the convergence of the estimation errors to a domain. This observer solves the infinite time convergence problem of neural observers with sliding mode compensation, and the chattering phenomenon of sliding mode observer.