Research On Strategies And Applications Of Multi-model Predictive Control For Complex Industry Processes

Model predictive control (MPC) coming from engineering practice has many merits such as lower demand for model matching, convenience to calculate on-line and higher control quality, so it was applied successfully in the industrial application. With more and more rigorous demands of control quality in industrial processes, MPC based on one model can't already satisfy the control requirements of the complex industrial processes. Research on multi-model predictive control(MMPC) is significant in theory and valuable in application because it can not only widen the applying range of MPC but of improve the control quality for complex industrial processes.Firstly, the dissertation summarizes the present researches and classifies MMPC into two groups: weighting MMPC and switching MMPC, witch each one can be sorted as two forms: by controllers and by models. The key of MMPC is not the MPC algorithm itself but how to select the weighting strategies and switching strategies. it is proposed that the weighting average to the multi-step moves is impactful method to switch controllers smoothly. Afterward, the study addresses the following topics:At the aspect of building multi-model bank, a simple effective method is presented for a class of industrial process. The maximum and minimum values of the parameters describing system dynamic behavior such as time-constant, model-gain and dead-time can be acquired from experiential knowledge and testing data, then the multi-model bank was set up by the means of dividing its extreme models composed of the maximum and minimum parameters via equidistance between sub-models. The predictive model is obtained by weighting the sub-models with the recursive Bayesian scheme. Simulation results demonstrate the efficiency of the method.At the aspect of control algorithm, the predictive functional control (PFC) was studied so as to reduce the calculation load of MMPC. Focusing on the first order plus dead time system with measurable disturbance, an improved PFC algorithm was proposed based on the ideal of Smith predictor, which emphasize the difference between control channel dead-time and disturbance channel dead-time. The algorithm was applied further to multiple variable systems. In addition, focusing on the MPC based on T-S fuzzy model, a new error compensating means is stated to simplify the calculation of the control moves. It has less computing load comparing with multi-step linearization method and better control quality comparing with one-step linearization method. They are all proved correct by simulations.In the sixth chapter, fuzzy gain scheduled MMPC was applied to the ALSTOM gasifier benchmark problem, in which the load is selected as scheduled variable and three models lying respectively at three operation conditions are select as sub-models. Simulation results, accordingly to the requirement of the benchmark problem, show that the method has best control performances. The ability to control the complex industrial processes for MMPC was qualified once more.