Objective To study error compensation techniques in high-precision pressure detection systems and improve measurement accuracy. Methods A comprehensive error compensation method based on neural networks was proposed, and a pressure sensor error model was established. An adaptive neural network compensator was designed. Results This method achieves effective compensation for nonlinear errors, and experimental results show that the systematic nonlinear error decreases from 0.25% FS to 0.015% FS and the maximum relative error from 0.38% to 0.022%. After temperature compensation, the temperature coefficient decreased to${0.002}\%\mathrm{{FS}}/{}^{\circ}\mathrm{C}$, which increased by${80}\%$compared to$\pm {0.01}\%$FS/${}^{\circ}\mathrm{C}$before compensation. The dynamic response time is in the${1.7}\sim {2.0}\mathrm{\;{ms}}$range, with a$-3\mathrm{\;{dB}}$bandwidth of${850}\sim {870}\mathrm{\;{Hz}}$. Conclusion The error compensation method proposed in this paper can effectively improve the measurement accuracy of pressure detection systems and provide new ideas for the development of high-precision pressure detection technology.

high-precision pressure detection  /  error compensation  /  neural networks  /  nonlinear correction  /  adaptive algorithms