Hybrid Modeling And Dynamic Compensator Inbuilt Nonlinear Predictive Control Methods Of Fermentation Processes

Fermentation is one of the most important technical elements of biological engineering and modern biotechnology. Because of the progress on biotechnology and increasing scale of industrial fermentation production, it became an urgent need to improve the fermentation process control performance and system robustness. Fermentation process is typically non-linear, non-stationary, high-dimension, time-varying, and lack of adequate prior knowledge. It is difficult to establish an accurate mathematical model to describe the process characteristics. As advancement of the fermentation process control performance requirements, the studies on hybrid modeling methods and efficient adaptive control strategies have important theoretical significance and potential application value.In this paper, the existing fermentation process modeling and process control methods are analyzed in detail. Aim at the problems in fermentation process control, the nonlinear systems with uncertainties, which include model mismatching and unknown dynamics, were mainly studied. The research issues include hybrid model structure, on-line fast modeling, robust state estimation, robust predictive control, etc. A simulation study of Penicillin fermentation was made to confirm the proposed methods.As the fermentation process is very complex process, it is difficult to obtain good results based on traditional modeling method. Therefore, the paper presents a hybrid state space model to describe a nonlinear system with uncertainties. By introducing local dynamics, makes the model not only has the advantages of general nonlinear systems, but also characterized a wider field of nonlinear process. This paper discusses the stability of the nonlinear system with unknown dynamics, and the corresponding compensation methods with an output feedback nonlinear model predictive control (NMPC). The overall implementation framework laid a solid theoretical basis.In the kernel-based online modeling studies, in order to enhance the support vector regression (SVR) modeling speed, this paper presented the geometric interpretation of support vector machine (SVM). It was proved that SVR and support vector classification (SVC) are the equivalence problem by duplicating the training samples in the feature space. Some of the geometric training algorithms were introduced to speed up SVR training, which are simpler and faster than other QP-based algorithms but only suit for SVC earlier. In addition, kernel independent component analysis (KICA) was introduced to decomposition unknown dynamics of the independent impact, and a novel in-place moving-window recursive least square SVM (RLS-SVM) algorithm with lower computational complexity was used to model and forecast the unknown dynamics.In the studies of filter-based adaptive controller design, the remodeling strategies based on hybrid model were discussed firstly. The controller design methods based on nonlinear quadratic regulator with loop transfer recovery (NQR/LTR) were studied. Further, based on robust particle filter (RPF) and unscented transformation based robust Kalman filter (UT-RKF) algorithm, two dynamic compensator inbuilt NQR/LTR algorithms were proposed, and gave the complete adaptive output feedback predictive control algorithm system of fermentation process.By the penicillin fermentation process control simulation experiment, the results show that, for SVR training on large-scale data in the same experimental conditions, the regression based on geometric algorithm was quite precise with faster computing speed and better convergence, and effectively improve the SVR for real-time processing. The comparative studies of EKF, RKF, UT-RKF and RPF for state estimation under different initial value, noise variance cases show that, UT-RKF algorithm has higher estimation accuracy and better numerical stability when the noise is Gaussian, and UT-RKF has lower computational complexity. Comparing with the open-loop control and NMPC without dynamic compensation, the proposed dynamic compensator inbuilt NMPC could track the optimal trajectory very well, and have a better control performance and robustness.This paper presents a dynamic compensator inbuilt NMPC system for fermentation process, it has good robustness and control performance, provides a significance method to improve the complex non-linear process control system performance, and has a broad application prospects.