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An adaptive double neural network with a dynamic surface control scheme is investigated for a micro-gyroscope in the presence of manufacturing errors and disturbances. A dynamic surface controller, where a first-order filter is introduced in each step, is proposed to reduce the parameters and the computational complexity. One radical basis function neural network is used to approximate the lumped dynamics of the micro-gyroscope. Another radical basis function neural network is designed to approximate a sliding-mode controller to compensate the approximation errors in order to weaken the influence of the approximation errors. Simulation studies prove the effectiveness of the proposed strategy, demonstrate the proposed strategy could reduce the chattering, shorten the tracking time, and improve the tracking performance.

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