Research On Predictive Control Method Of Marine Supercharged Bolier Drum Water Level

Supercharged boiler plays an important role in marine steam power plant and it has the advantages of small size, light weight, big power, economy and high reliability and so on. To promote the development of marine steam power and improve the automation level of supercharged boiler, it is urgent to carry out advanced control method, which will ensure supercharged boiler safety and stability operation in the shipbuilding industry. As a superior control algorithm, predictive control shows good performance in process control applications. The thesis relies on the actual scientific research for the topic research background and is based on the evaporation system of marine supercharged boiler. For reducing the amount of algorithm and improving the real-time behavior, the following research is carried out:Firstly, from the establishment of supercharged boiler system simulation platform point of view, working mechanism of the evaporation system is comprehensively analyzed. Simplifying the physical model, dynamic mechanism model is established by using mass balance and energy balance equation of steam and water two-phase. The simulation results reflect dynamic response in the different conditions and accord with basic law and process mechanism for providing the foundation on the follow-up study of drum water level control method.Secondly, to improve marine supercharged boiler drum water level system's rapidity, the cascade GPC control algorithm has been improved and a practical cascade generalized predictive control algorithm based on grey predictive model is put forward. The controller uses a simple gray forecasting model which reduces the amount of calculation compared to CARIMA model of generalized predictive control and realizes effective control of a particular condition for drum water level. This method combines the advantages of the predictive control, cascade control, gray theory and PID control, and the method is simple and easily used in real systems.Thirdly, according to the operational characteristics of a wide range of variable conditions, multi-model predictive control strategy based RBF neural network dynamic compensation is proposed. The fixed models are established on typical operating points and the RBF neural network model is used to compensate the model error. The models are swiched in time to adapt to the changes of dynamic characteristics. The method fully considers that a single fixed model can not adapt to the changes in a wide range of operating conditions.The combination with predictive control and RBF neural network greatly improves the system robustness and stability.Finally, for the drum water level with the non-linear, time-varying and uncertain environment, sliding mode variable structure predictive control strategy based on T-S fuzzy model is designed. The method uses T-S fuzzy model for approaching the nonlinear model of the system and applies the strong robustness of sliding variable structure control to enhance the control behavior of predictive control. The simulation results reflect the new nonlinear predictive control strategy has a very good robustness.