Two-Stage Neural Observer for Mechanical Systems

This paper proposes a novel velocity observer which uses neural network and sliding mode for unknown mechanical systems. The neural observer in this paper has two stages: 1) a dead-zone neural observer assures that the observer error is bounded and 2) a super-twisting second-order sliding-mode is used to guarantee finite time convergence of the observer. With sliding mode compensation, the two-stage neural observer ensures finite time convergence, and reduces the chattering during its discrete realization.