Research And Applications Of Multi-variable Constrained Model Predictive Control Algorithm And Software

Model predictive Control (MPC) technology has gained considerable attention for its good performance since the generation of the MPC in the late 1970s. The great economic benefits by using the MPC packages make the people's focus attention upon them. In the published papers, only introduction of the functions and applications of the packages can be found, little technology is introduced in detail. In order to advance the technology of Industrial control in our country, the only way is to develop our own commercial industrial control package.In the dissertation, we investigate the Framework, key algorithm and technology of FrontAPC, the model predictive control package developed by ourselves, and its application in the field of process control. According to the implementation and practical requirement, we research the local steady-state optimization and dynamic optimization algorithm in MPC package.This dissertation is composed of follows:1. The Framework, functions and technology in FrontAFC, the MPC package developed by ourselves, is presented.2. In the part of steady optimization in FrontAPC, a multi-priority algorithm is given. This algorithm satisfies the optimized objectives sequentially according to the priorities defined by operator, so avoids the problem of multi-objectives, which caused by putting all optimized objectives in a objective functions. Also this algorithm can use up the degrees of freedom of system, and acquire unique solution.3. In the Part of dynamic optimization in FrontAPC, author give a range dynamic optimization algorithm and its transformation. It adopts developed predictive model to shorten the predictive time and eliminate the truncation error, adopts range control to avoid the frequent change of inputs, adopts block technology to reduce the dimension of QP, uses performance ratio to make the controller's tuning sample and intuitionistic.4. A multivariable fast constrained DMC algorithm is presented, which adopts improved model to remove truncation error, and computes one control move to optimize one point of future horizon for each output. Compared to traditional DMC algorithm, this algorithm reduces calculational burden greatly, and loses less performance. It has practical significance for some factories that are short of money to achieve advanced control.5. The application of Model Predictive Control to PTA equipment is presented. For the control of slurry density system and advanced control and optimization of solvent dehydration tower, author does a lot of work in system analysis, the design of control scheme, the identification of models, and the implement of controller. The applications to PTA equipment have acquired big economic benefit.