Application Of Robust Predictive Controller Based On T-S Fuzzy Model On Temperature Control Of Beer Fermentation

Beer fermentation is the most important part in brewery and there are so many factors which will affect the final quality of fermentation during the fermentation process, such as temperature, pressure, concentration and so on. Temperature is the key factor and changes of temperature will influence the changes of pressure and concentration. So the fermentation temperature must be able to follow the temperature process curve. Beer fermentation process is a nonlinear, uncertainty, multi-disturbance process. There are so much internal parameter changes and external disturbances in the fermentation process, such as yeast characteristics and the change of coolant temperature and pressure. The fermentation process involves many complex physical and chemical reactions and the materials involved are very wide. So it is difficult to establish a precise mathematical model. The conventional control methods can't meet the requirements of the optimal tracking temperature curve. In this paper, the robust control which can make sure the system stability and the predictive control which has thought of receding optimization are combined. The robust predictive controller which is applied to the fermentation process based on T-S fuzzy model is proposed. This controller not only effectively deals with these characteristics of ferment system, but also can really be applied to the actual production with some advantages such as strong practicability and good control effect. The research results and main contents of this paper include the following aspects:(1) The beer fermentation process and temperature process curve are described in detail. This paper studies focus on the control object which has those characteristics, such as nonlinear, uncertainty, multi-disturbance and so on and analyzes the difficulty of the system. The status of the fermentation process control research is described and it provides the design significance to the controller which be designed in this paper.(2) The T-S fuzzy model which is used to approximate the nonlinear system is introduced. According to the fuzzy rules in T-S model, the parallel distribution compensation technique is adopted to obtain the local state feedback robust controller by the Linear Matrix Inequality, which satisfies the H_? robustness. Then the robust controller is obtained by weighting each local controller. But the state feedback controller is limited by the condition that the state must be measured completely. Therefore combines the thought of receding optimization in predictive control and uses the thought of output feedback to solve this problem. To be directed against the time-varying of local controller parameters, just know the boundary value of the non measurable state at each sampling time. Then solves a set of constraints of the LMI and weight them to get the final robust predictive controller.(3) Modeling the beer fermentation process by mass conservation equation and heat conservation equation. After getting the dimensionless model and transforms it into T-S model. Then the robust predictive controller is applied to it. The simulation result shows that difficulties in fermentation process are solved and meet the control requirements.