Multi-target optimization prediction control method based on trapezoid interval soft constraint

Abstract

The invention discloses a multi-target optimization prediction control method based on trapezoid interval soft constraint. The multi-target optimization prediction control method comprises a step1, establishing of a prediction model; step2, calculation of prediction out; step 3, feedback of correction; step 4, building of a trapezoid interval outside a tolerance interval; step 5, calculation of an optimization variable (img file+DDA0001238247080000011. Tif wi=70 he=47); step 6, building of a multi-target function; step 7, conversion of a multi-target problem into a single-target problem by using an omega-constraint method; step 8, acquisition of an optimal control increment by using a sequential quadratic programming algorithm. When the controlled variables exceed the tolerance interval, the controlled variables can enter the tolerance interval quickly to guarantee product quality. The building of the multi-target function is used to effectively prevent the mutual coupling among the various controlled variables, and the interval range of the constraint function is optimized by adopting an iterative algorithm, and then rapidity of operation of a system is guaranteed. By adopting combination of a set value control and a trapezoid interval soft constraint, the system can be operated at an ideal target value, and at the same time, the robustness and the freedom degree of the system area guaranteed to the greatest extent.

Description

technical field [0001] The invention relates to the field of industrial control, in particular to a multi-objective predictive control method based on trapezoidal interval soft constraints. Background technique [0002] Predictive control algorithm is a control method that can deal with system constraints, performance indicators and multivariable optimization problems. It has been well applied in the field of industrial control because of its advantages of small calculation and good robustness. In the industrial process, the controlled object is often a multiple-input multiple-output system. The control system no longer only puts forward control requirements for a certain parameter or a certain performance of the production process, but proposes economical, rapid, Comprehensive performance index control such as environmental protection. Due to the influence of the industrial environment and the complexity of the system itself, ordinary predictive control methods are difficu...

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Problems solved by technology

[0004] To sum up, for the CV with strict control indicators in the industrial process, one is to adopt the traditional set value method, but its shortcomings of low degree of freedom and poor robustness still need to be solved; When the mutual coupling between the control variables will cause errors in the solution of the objective function; third, when the objective function is established, the simple superposition of the control variables will increase the computational complexity of the objective function and bring difficulties to the solution of the objective function; The fourth is various improved interval control algorithms. In the early stage of the control function, the CV fluctuates violently, but no effective control action is taken in time to make it quickly enter the tolerance interval, which affects product quality.

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