Model Parameter Self-Correcting Fuzzy Proportional–Integral–Derivative Adaptive Control of the Integration Time of Raman Spectrometers

Abstract

The control of the integration time of Raman spectrometer is a crucial factor for the acquisition quality of Raman spectrum. However, due to the coupling effect of linear and nonlinear factors such as the intensity of Raman scattering effect, acquisition noise, fluorescence effect, and spectrometer optical path, it is rather difficult to fast control the integration time, which influences the acquisition efficiency. In this article, aiming at the nonlinear automatic optimal control of the integration time, a model parameter self-correcting fuzzy proportional–integral–derivative adaptive (MPSC-FPIDA) control method is proposed. By constructing an initial parameter prediction model and the fuzzy PID controller, two processing stages, namely, Raman spectrum intensity detection and fuzzy PID iterative control, are carried out, with which the automatic calculation and optimal control of the integration time of the Raman spectrometer are achieved according to the set point of spectrum intensity or signal-to-noise ratio (SNR). By introducing a parameter self-correcting factor of the initial model, the adaptive correction of the parameters of the initial prediction model of Raman spectrometers with different optical laser wavelengths is realized, which ensures the universality of this method to spectrometers of different types. The experiment demonstrates that when it comes to acquisition of Raman spectrum for various samples, the method proposed in this article can accurately calculate the optimal integration time within 2–3 iterations and automatically adapt to the performance parameters of different Raman spectrometers, of which the control performance is as good as that of modeling spectrometers. This shows that the method features fast convergence speed, strong robustness, and versatility. In addition, compared with commonly used linear control methods, it can effectively suppress the coupling effect of linear and nonlinear factors, which improves the efficiency of spectrum acquisition and enhances the control performance by over 50% as well.