Interval prediction control modeling and optimizing method based on soft constraints

Abstract

Provided is an interval prediction control modeling and optimizing method based on soft constraints. The control method comprises the following steps: (1) a quadratic performance index including a constraint item, a control item and an economic item is established based on a process prediction model; (2) whether an overall optimization method is feasible is judged by solving a slack variable; (3) a method for solving a soft constraint slack variable when a control model output constraint is not feasible is provided, and adjustment of the range of a feasible region when an interval prediction control model output constraint is not feasible is realized; and (4) a boundary feasible sequence quadratic programming method is adopted to solve the problem that poor initial point selection causes calculation amount increase of the method or difficulty in finding an optimal solution, the problem that the positive definiteness of a Hessian matrix is destroyed due to the influence of round-off error in calculation, and the like, and to figure out the optimal control input. A complicated multivariable system control model can be established, the control law can be solved accurately and quickly based on soft constraint adjustment, and good control on a multivariable system can be achieved.

Description

technical field [0001] The invention belongs to the field of process control, in particular to a modeling and optimization method for interval predictive control based on soft constraints. Background technique [0002] In recent years, with the increasing scale of industrial process systems, increasingly tight energy supply, and rising energy prices, producers no longer only require control of a certain parameter or a certain performance of the production process, but according to Production requirements and human will put forward comprehensive performance index control such as economy and rapidity. However, due to environmental factors and the complexity of the system itself, there are many constraints in the actual system, so the common predictive control method is difficult to meet the control requirements. [0003] In the actual industrial control process, if the constraints of the system area are taken into account in the control objectives, the feasible set of the opt...

AI Technical Summary

Problems solved by technology

These algorithms require multiple iterative calculations when solving the control model, so the amount of calculation is very large, and the program is cumbersome and complicated to run, which makes it impossible to achieve real-time control when the actual system runs quickly.

[0004] To sum up, the existing methods either have limitations or are computationally intensive, making it difficult to apply them in practice.

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