Micro-machined gyroscope (MMG) has wide application prospects in the economic and military fields, especially in the military fields where weight and volume are important value. However, the precision of MMG are affected by the mechanical thermal noise, the Coriolis force of the driving mode coupled by the sensing mode, the quadrature error and the coupling damping. It is necessary to establish the more realistic gyroscope structural model and improve the closed loop driving control. In this paper, the structural model of MMG which can describe the mechanical thermal noise, the Coriolis force of the driving mode coupled by the sensing mode, the quadrature error and the coupling damping is proposed firstly. For the proposed gyroscope model, the closed-loop driving control based on the reinforcement learning PID algorithm is applied to improve the precision of MMG. The simulation results show that the reinforcement learning PID controller can fulfill the requirements of the rapid start-up and the overshoot reducing. The control system can stabilize the amplitude and track the resonant frequency of the driving mode. It is important for the applications of MMG.
The near-sensing detection system based on the software-defined radio (SDR) platform is widely used due to its versatility and algorithm reconfigurability. Frequency modulation continuous wave (FMCW) detection has high precision and strong anti-interference ability. To solve the problem where the near-sensing detection system has a large fixed error when the modulation frequency deviation is small, a ranging method based on spectral feature matching is proposed in this paper. Compared with traditional algorithms, such as harmonic detection, zero-padding Fast Fourier Transform (FFT) and beat frequency correction, the proposed algorithm makes full use of the spectral characteristics of the beat signal. By establishing a spectral feature library of different distances, a matching evaluation function is given to transform the problem of distance recognition into a feature matching problem. The accuracy of the algorithm is verified by comparing the ranging error of the feature matching algorithm and other ranging algorithms at different signal-noise ratio (SNR) levels.
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