Researches On Predictive Control Algorithms For Complex Systems And Applications

Predictive control of nonlinear systems is one of the most important problems in control theory. From the standpoints of theory and practical applications, this dissertation is devoted to predictive control strategies for time-varied linear system and complex nonlinear systems. Several new predictive control methods are presented. The main research works are as follows:1. A survey of model predictive control on theory and applications is introduced. The main research results are given.2. Multiple model predictive control strategy is presented based on different equilibrium point state of a nonlinear pH process, the local linear model is recognized according to maximum membership function rule and the corresponding controller is chosen, and the key problem of multiple model predictive control switching is solved.3. Multiple Models Predictive Function Controller (MMPFC) based on membership function weighting is proposed for disturbance in multiple models switching. Simulation results in a continuous stirred tank reactor (CSTR) show that controlled dynamic process is more smoothly than the multiple models switching control strategy.4. A new strategy of adaptive predictive function control based Laguerre model is suggested. It combines advantages of Laguerre non-parameter and parameter model, adaptive control on-line modifying parameter of model with predictive function control fast tracking. The drawback of knowing in prior time-delay and order in general adaptive predictive function control is overcame.5. Multiple models switching control algorithm is compared for a class of nonlinear systems, the sudden changes of model parameters and uncertain linear systems. The advantages and disadvantages of switching control algorithm in chapter 3, weighted-sum algorithm of the linear model by using model validity functions hi chapter 4 are discussed. Simulation result of aCSTR show that the proposed switching control algorithm is indeed superior to the conventional counterpart.6. The control strategy based on global feedback linearization for nonlinear systems is developed for MIMO bilinear systems. Using the linearized input/output decoupling model, the Predictive Function Controller (PFC) is designed. The simulation results of the paper machine pressured headbox model show that the performance of the derived control strategy is good.7. The application of multiple models predictive function control in continuous fermentation processes and drying fluid-bed is studied.The dissertation concludes with a summary and perspectives of future research of nonlinear predictive control in complex system.