A Study On Predictive Control Algorithm And Its Simulation

As a computer control algorithm, Model Predictive Control (MFC) has many successful applications in industry control process and has become an important advanced process control strategy. It has three basic characteristics: model prediction, receding horizontal optimization and feedback correction.Compared with other control algorithms, MFC has its own features:a. Not require precise model prediction, model can be obtained by simple experiment.b. By using receding horizontal optimization strategy, the uncertainty is compensated for model mismatch or disturbance existed.c. The algorithm can be easily extended to constrained and large delay processes and can deal with constrained multivariable problems efficiently.This thesis focuses on two algorithms: Dynamic Matrix Control (DMC) and Predictive Function Control (PFC). For DMC, its application is studied. A complex process is simulated based on multivariable DMC. For PFC, the algorithm is improved and control strategy is studied. The thesis includes following contents:(1) A survey of the development and status of model predictive control is given. An introduction of related control strategies is also given.(2) Based on the detailed description of the principles and algorithm of the PFC, it can be verified that the PFC has the following advantages: simple algorithm, less on line calculation, high control precision and disturbance attenuation by the theory analysis and simulation.(3) PFC algorithm based on pole-placement is given. Poles are assigned to given position by selecting suitable weighted polynomial and adding it in optimal performance index. The expected closed loop response is obtained.(4) Neural net decoupled PFC algorithm is discussed. Multivariable process is decoupled with neural net compensation. The decoupled subsystem is applied tomsingle variable PFC. The simulation result shows the algorithm has better tracking performance. It is suitable for resolving optimization and control of multivariable process in some extent.(5) Combined with coordination control algorithm, multivariable and two-layer coordination PFC is studied. The result of simulation shows the algorithm is an efficient way to deal with the multivariable system.(6) A typical multivariable nonlinear system ~ CSTR model is simulated by applying multivariable DMC with Simulink. The simulation result shows the algorithm has a better tracking and control ability for multivariable process.Finally, based on the summarization of the research results in this thesis, further research areas about MFC are discussed.