This article presents an incremental linear model predictive control (ILMPC) scheme based on the global linearization prediction neural network (GLPNN) model to compensate nondispersed and dispersed hysteresis. Hysteresis seriously weakens the tracking performance of piezoelectric actuators (PEAs), and the existing modeling and control methods mainly focus on the compensation of nondispersed hysteresis. This article investigates the GLPNN model of PEA to capture hysteresis behavior over a wide frequency band. The model is precisely identified by a specially designed neural network related to historical inputs, which is data driven. The modeling results verify that the modeling error is less than 10% of the previous models. Based on the identified model, linear control theory can be utilized for nonlinear hysteresis compensation. The real-time ILMPC scheme is proposed to achieve hysteresis compensation, which avoids complex nonlinear optimization problems. The experimental results indicate that the new compensator advances in eliminating hysteresis over a wide frequency band. Especially, the tracking error is significantly lower than previous studies in the dispersed band, and the relative hysteresis loop error is reduced by 55.24%. Different from the existing compensation strategies for limited bandwidth, the proposed modeling and control methods exhibit better tracking performance for PEAs over a wide nonresonant frequency band.

Flexible pressure sensors have drawn growing attention in areas like human physiological signal monitoring and human–computer interaction. Nevertheless, it still remains a significant challenge to guarantee their long-term stability while attaining a wide detection range, a minute pressure testing limit, and high sensitivity. Inspired by the wrinkles on animal skins, this paper introduces a flexible pressure sensor with wrinkled microstructures. This sensor is composed of a composite of reduced graphene oxide (rGO), single-walled carbon nanotubes (SWCNTs), and polydimethylsiloxane (PDMS). After optimizing the proportion of the composite materials, the flexible pressure sensor was manufactured using highly efficient heat-shrinkable films. It has a sensitivity as high as 15.364 kPa⁻¹. Owing to the wrinkled microstructures, the sensor can achieve an ultra-wide pressure detection range, with the maximum reaching 1150 kPa, and is capable of detecting water wave vibrations at the minimum level. Moreover, the wrinkled microstructures were locked by PDMS. The sensor acquired waterproof performance and its mechanical stability was enhanced. Even after 18,000 cycles of repeated loading and unloading, its performance remained unchanged. By combining with an artificial neural network, high-precision recognition of different sounds and postures when grasping different objects was realized, with the accuracies reaching 98.3333% and 99.1111%, respectively. Through the integration of flexible WIFI, real-time wireless transmission of sensing data was made possible. In general, the studied sensor can facilitate the application of flexible pressure sensors in fields such as drowning monitoring, remote traditional Chinese medicine, and intelligent voice.

Real-time monitoring of various aeroengine parameters is necessary to ensure the stable operation of aircraft engines and provide timely feedback on the operating status of the engine. The monitoring process requires high-temperature sensors that operate at temperatures exceeding 600 °C. Traditional sensors are limited at these temperatures. In this paper, a combined sensor based on multi-sensor technology is presented for health monitoring in high-temperature environments. The combined sensor consist of a platinum resistor temperature sensor and a triangular shear mode vibration sensor. The combined sensor performance was simulated and tested. The average sensitivity was 3.29 pC/g when tested in the frequency range of 80–850 Hz from 25 to 800 °C. The sensitivity remained stable after being maintained at 800 °C for 6 h. Following platinum-resistance temperature compensation, the acceleration measurement error was reduced to 10 ^-2 –10 ^-3 g. The sensor can solve the problems of unstable measurement and temperature drift of traditional sensors at 800 °C and be applied in fields such as the aerospace, metallurgical, and petrochemical industries.

A binuclear macroheterocyclic complex Zn₂(FcL)₂ based on ferrocene-thiocarbazide (FcL) was synthesized. X-ray diffraction analysis shows that it belongs to the monoclinic system P2(1)/n space group, with a = 1.35379(17) nm, b = 2.6026(3) nm, c = 1.8693(2) nm, β = 95.687(2)° and final R indices R = 0.0796 and Rw = 0.2119. Two Zn(II) ions and two FcL ligands form a 22-membered macroheterocycle, further extended to an infinite 1-D helical chain by intermolecular hydrogen bonding. Spectral properties of the title complex and electrochemical properties of the ligand FcL are also discussed.

Strain measurements are vital in engineering trials, testing, and scientific research. In the process of signal acquisition, baseline drift has a significant impact on the accuracy and validity of data. Traditional solutions, such as discrete wavelet transform and empirical mode decomposition, cannot be used in real-time systems. To solve this problem, this paper proposes a Transformer-based model to eliminate the drift in the signal. A self-attentive mechanism is utilized in the encoder of the model to learn the interrelationships between the components of the input signal, and captures the key features. Then, the decoder generates a corrected signal. Meanwhile, a high-precision strain acquisition system is constructed. The experiments tested the model’s ability to remove drift from simulated voltage signals with and without Gaussian noise. The results demonstrated that the transformer model excels at eliminating signal baseline drift. Additionally, the performance of the model was investigated under different temperature conditions and with different levels of force applied by the electronic universal testing machine to produce strain. The experimental results indicate that the Transformer model can largely eliminate drift in dynamic signals l and has great potential for practical applications.

The internal hot-end components of aero-engines operate in a high–temperature environment are susceptible to cracks or falling blocks owing to thermal fatigue and other effects. The rapid changes in heat flux at these defects can lead to engine failure. We propose a miniaturized sensor capable of monitoring heat flux change in a high-temperature environment. The sensor is based on a thermopile, connected in series with the platinum–10% rhodium/ platinum (Pt–Rh10/Pt) thermocouples as the basic unit on a ceramic plate sized 10 mm × 10 mm × 1 mm. Nanocomposite silicon dioxide is adopted as a thermal insulation material to generate a temperature difference at the hot and cold junctions, which is distributed in the center and results in a thermal voltage output in the millivolt range. The heat flux sensor (HFS) can operate at temperatures up to 1000 °C with a good repeatability. The output voltage at a heat flux of 189.6 kW/m ² is 0.4113 mV, and the dynamic response time of HFS is 0.25 s. The screen printing process enables the batch preparation of micro heat flux sensors for high-temperature device surfaces with small areas.