Experimental Investigation of Specified-Time Adaptive Control for MEMS Gyroscopes With Sampling Constraints

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Abstract
In this paper, we present a novel specified-time adaptive output-feedback control scheme for microelectromechanical system (MEMS) gyroscopes subjected to sampling constraint. The primary contribution of this study lies in the development of a computationally efficient Unknown System Dynamics Estimator (USDE) based on the invariant manifold principle, which accurately estimates total system uncertainty with minimal computational cost. To overcome the potential corruption of velocity signals due to measurement noise and the inherent resource limitations of MEMS gyroscope platforms, we integrate a variable-threshold event-triggered state observer into the USDE. This innovative architecture replaces actual velocity measurements with their estimated values, ensuring high estimation accuracy while minimizing computational burden. Additionally, to meet the stringent response speed requirements of MEMS gyroscopes in practical applications, we introduce a Specified-Time Funnel Control (STFC) scheme. This control approach guarantees that the displacement error converges to the desired value within a predefined time, simultaneously constraining the error bounds, which results in superior transient and steady-state performance of the system. The novelty of this work lies in the combination of the USDE with the event-triggered observer and STFC, providing a robust solution for MEMS gyroscope control under stringent resource constraints. The effectiveness and superiority of the proposed method are rigorously validated through both theoretical stability analysis using Lyapunov methods and extensive simulation and experimental results, demonstrating its significant improvement over existing approaches.
Funding Information
  • Science and Technology Innovation Key Research and Development Program of Chongqing (CSTB2025TIAD-STX0016)
  • National Key Research and Development Program of China (2022YFB3205000)
  • National Natural Science Foundation of China (52475586)
  • National Natural Science Foundation of China (U2230206)

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