With the increase in the application of micro-electromechanical system (MEMS) accelerometers, their accurate calibration is increasingly essential to reduce the application error. Aiming at the high accuracy calibration requirements of scale factor, bias, and nonorthogonalities, we propose a novel calibration method to obtain highly accurate parameters, especially in the scenario without turntable. First, a twelve-parameter model of accelerometer output data is used for analysis. The static data characteristics of the six specified positions of the accelerometer were then analyzed. Finally, based on these characteristics, a novel calibration method performed using an iterated parameter correction process is proposed. Repeatable simulations under different biases show that the calibration accuracy of the proposed method is not affected by the bias value to be calibrated, and that the calibration results have consistent accuracy. The estimation error of bias and relative error of scale factor are less than 0.5 mg and 0.05%, respectively, and the accuracy of the non-orthogonal elements of the calibration matrix S and K are 0.003 and 0.006, respectively. In addition, the calibration results of the laboratory MEMS accelerometer and the triaxial accelerometer in ADIS16488 demonstrate the effectiveness of the proposed method.

The force-to-rebalance (FTR) closed-loop detection method is commonly used to expand the bandwidth (BW) for micro-electro-mechanical system (MEMS) gyroscopes with low-frequency split and high-quality factors. However, the relationship between the BW and output noise is often incompatible; thus, reducing the detection accuracy of the gyroscope. This paper presents an analysis of the BW and noise spectra under modulation-demodulation FTR gyroscopes. The expressions for the BW and the noise-equivalent rate (NER) spectrum are derived to explore the effects of the loop gain on the BW and NER. It is demonstrated that the gain of the amplifier circuit is maximally transferred to the vibration displacement signal conversion part, which can reduce the output noise without affecting the BW. The simulation and experimental results on the Cobweb-like disk resonator gyroscope show that the derived expressions of BW and NER are correct, and the noise optimization method is effective, which provides an idea for the realization of a high-precision MEMS gyroscope.

This paper presents a dual quantization lateral-axis capacitive electromechanical sigma-delta modulator (EM-Σ Δ M) vibratory wheel gyroscope under mode-matched condition. A symmetric wheel gyroscope structure was designed, fabricated, and wafer-level packaged under vacuum. The EM-Σ Δ M interface loop adopts a fourth-order multifeedback and local resonator Σ Δ M noise shaping structure, taking advantage of single-bit and multibit quantization in a single-loop modulator. To evaluate the influence of quantization noise, various numbers of bits of the multibit quantizator were compared and the optimal number of bits was calculated. The interface circuit system was implemented in hardware based on a low-noise front-end application-specified integrated circuit and a back-end discrete-time EM-Σ Δ M realized in a field-programmable gate array chip. The system clock was generated using a self-clocking scheme, which allows interfacing to gyroscope with a wide range of drive frequencies. Experimental evaluation indicated that the digital gyroscope system achieved a scale factor of 2073LSB(4.1 mV)/deg/s, with a nonlinearity of 0.046%, a noise floor of 3.1 m°/s/√ Hz within a bandwidth of 100 Hz, and a bias instability of 1.3 deg/hr. [2017-0081]

In order to solve the problem where existing mode-matching methods in microelectromechanical systems (MEMS) vibrating gyroscopes fail to meet real-time and reliability requirements, this paper presents a novel method to accomplish automatic and real-time mode-matching based on phase-shifted 45° additional force demodulation (45° AFD-RM). The phase-shifted 45° additional force signal has the same frequency as the quadrature force signal, but it is phase-shifted by 45° and applied to the sense mode. In addition, two-way phase-shifted 45° demodulations are used at the sense-mode detection output to obtain a phase metric that is independent of the Coriolis force and can reflect the mode-matching state. Then, this phase metric is used as a control variable to adaptively control the tuning voltage, so as to change the sense-mode frequency through the negative stiffness effect and ultimately achieve real-time mode-matching. Simulation and experimental results show that the proposed 45° AFD-RM method can achieve real-time matching. The mode frequency split is controlled within 0.1 Hz, and the gyroscope scale factor, zero-bias instability, and angle random walk are effectively improved.

This study presents a new real-time calibration algorithm for three-axis magnetometers by combining the recursive least square (RLS) estimation and maximum likelihood (ML) estimation methods. Magnetometers are widely employed to determine the heading information by sensing the magnetic field of earth; however, they are vulnerable to ambient magnetic disturbances. This makes the calibration of a magnetometer inevitable before it is employed. In this paper, first, a complete measurement error model of the magnetometer is studied, and a simplified model is developed. Then, the real-time RLS algorithm is introduced and discussed in detail, and the unbiased optimal ML is utilized to improve the accuracy of the parameter estimation. The proposed algorithm is advantageous in correcting the parameters in real time and simultaneously obtaining unbiased parameter estimation. Finally, the simulation and experimental results demonstrate that both the accuracy and computational speed of the proposed algorithm is better than those of the widely used bath-processing method. Moreover, the proposed calibration method can be adopted for calibrating other three-axis sensors.

Devices used to set and control the environmental temperature are critical to the performance of gas-sensitive material analyzers, which use silicon microcantilevers to characterize the gas-sensitive materials. This paper describes a novel microtemperature-control device that uses a double Peltier structure to replace the traditional refrigerant temperature control system. A proportional-integral-derivative (PID) algorithm is used to achieve accurate and fast temperature control, with a long short-term memory (LSTM) network trained to identify the nonlinear dynamics of the Peltier system. A neighbor hybrid mean center opposition-based learning particle swarm optimization (NHCOPSO) algorithm is proposed to optimize the PID controller. The LSTM network identification is obviously better than that of previous Peltier system identification methods, and the NHCOPSO algorithm is found to be superior to other improved PSO and evolutionary algorithms on benchmark functions and in PID parameter optimization. Experimental results show that the proposed temperature control device greatly improves the accuracy and efficiency of gas-sensitive material analysis with a temperature control range of -40 to 180°C, a temperature control tolerance within ±0.05°C, a maximum heating rate of 20°C/min, and a maximum cooling rate of -10°C/min.