Output recurrent wavelet neural network-based adaptive backstepping controller for a class of MIMO nonlinear non-affine uncertain systems

Study on soft sensor modeling method for sign of contaminated fermentation broth in Chlortetracycline fermentation process

During Chlortetracycline fermentation, contamination of fermentation broth by non-target bacteria is an unavoidable problem. There is no online analytical instrument to determine whether the fermentation broth has been contaminated. Only the results of manual sampling analysis can be used to determine whether the fermentation broth is contaminated. This analysis process usually takes several hours. In order to predict online whether the fermentation broth is contaminated by non-target bacteria, a soft sensor modeling method for the signs of contamination in Chlortetracycline fermentation broth was proposed in this paper. Based on recursive wavelet neural network (RWNN) and Gaussian process regression (GPR) method, the soft sensor model of online measurable parameters and total sugar content of fermentation broth was established. By deeply analyzing the correlation between the total sugar content (it is a parameter that is difficult to measure online) of fermentation broth and the signs of bacterial contamination during fermentation, a soft sensor model was established combining with the correlation between the total sugar content of fermentation broth and the symptoms of bacterial infection, and the symptoms of non-target bacterial infection of fermentation broth were predicted. Based on the field data of the fermentation process, the different signs of Chlortetracycline fermentation were predicted for the fermentation broth uninfected with non-target bacteria, infected with bacilli and infected with phages. The experimental results showed that the proposed soft sensor model could be used to predict the occurrence of contamination during Chlortetracycline fermentation. Based on the field data, the validity of the modeling method is verified. The proposed soft sensor model of signs of bacterial contamination can be used to predict the occurrence of bacterial contamination in Chlortetracycline, Penicillin and related biological fermentation processes. So that the site operators can take effective measures in time to reduce production losses to a minimum.

Study on online soft sensor method of total sugar content in chlorotetracycline fermentation tank

In order to solve the problem that the total sugar content of the chlortetracycline fermentation tank can not be automatically detected online, a prediction method which combines the output recursive wavelet neural network and the Gauss process regression is proposed in this paper. A soft sensor model between the measurable parameters (inputs) and the total sugar content (output) of the chlortetracycline fermentation tank was established. The soft sensor model was trained by self updating algorithm. Based on field data, the accuracy and generalization ability of the soft sensor model were analyzed. It is shown that the prediction accuracy of the combined model proposed in this paper is better than that of other single models. The results demonstrate the superiority of the method, and MRE and RMSE are used to evaluate the performance of the soft sensor model. It shows that the prediction precision of the soft sensor model based on ORWNN-GPR combination is relatively high in the long period of fermentation, and is suitable for on-line prediction of the total sugar content of the chlortetracycline fermentation tank. The soft sensor method can effectively reduce the labor intensity of the analysts and saves the production cost for enterprise.

A wavelet neural control scheme for a quadrotor unmanned aerial vehicle

Wavelets are designed to have compact support in both time and frequency, giving them the ability to represent a signal in the two-dimensional time-frequency plane. The Gaussian, the Mexican hat and the Morlet wavelets are crude wavelets that can be used only in continuous decomposition. The Morlet wavelet is complex-valued and suitable for feature extraction using the continuous wavelet transform. Continuous wavelets are favoured when high temporal and spectral resolution is required at all scales. In this paper, considering the properties from the Morlet wavelet and based on the structure of a recurrent high-order neural network model, a novel wavelet neural network structure, here called a recurrent Morlet wavelet neural network, is proposed in order to achieve a better identification of the behaviour of dynamic systems. The effectiveness of our proposal is explored through the design of a decentralized neural backstepping control scheme for a quadrotor unmanned aerial vehicle. The performance of the overall neural identification and control scheme is verified via simulation and real-time results.This article is part of the theme issue 'Redundancy rules: the continuous wavelet transform comes of age'.