Deep diagnostic method of performance reduction of predictive control model

Abstract

The invention discloses a deep diagnostic method of performance reduction of a predictive control model. The deep diagnostic method of the performance reduction of the predictive control model includes: using a production run data calculation process disturbance signal, and then using production data and a step response coefficient of a design stage of a predicting controller to calculate model prediction error, and judging overall performance advantages and disadvantages of the predictive control model through a model performance index built through the production data and the step response coefficient of the design stage of the predicting controller. The deep diagnostic method of the performance reduction of the predictive control model further includes: for the model deteriorative in performance, using a method of removing input variables one by one to calculate a new model performance index, judging sub model performance corresponding the removed input variables through model performance index change situations, and thereby achieving monitoring for the performance of each sub module. The deep diagnostic method of the performance reduction of the predictive control model only uses the production process data and design data, and thereby not only can evaluate the overall performance of the multivariable predictive control model, but also can further evaluate the performance of all the sub modules corresponding to the input variables, provides a suggestion for an engineer to maintain a control system, and can substantially reduce maintenance cost of the predictive controller.

Description

technical field [0001] The invention belongs to the field of performance monitoring of industrial predictive control systems, and relates to a predictive model monitoring technology based on model performance indicators and in-depth inspection of removed input variables. Background technique [0002] As the dynamic characteristics of the production process change over time, the industrial model predictive control (MPC) system usually needs to be maintained less than one year after it is put into operation. The maintenance process requires object remodeling, which has a long cycle and high cost, and usually requires the shutdown of the production process. , causing great economic losses. Moreover, there is currently a lack of effective analysis methods to judge whether it is necessary to remodel the entire object before maintenance, or whether remodeling will definitely improve the overall control performance. In order to avoid unnecessary investment in remodeling and contro...

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Problems solved by technology

[0005] For the evaluation of the performance of the prediction model, the above research work has the following problems: (1) Most of the actual industrial processes are multi-variable systems with multiple input and multiple output (MIMO), and each output variable corresponds to each input variable. The model is a single input Single output (SISO) model, the current research work only gives the evaluation conclusion of whether the performance of the whole model is degraded, but engineers are more interested in which model performance degrades in which variable leads to the degraded control performance, so that in the control system Reduce workload and save costs during maintenance; (2) The definition of performance indicators often requires difficult-to-obtain process mechanism knowledge or object characteristic testing, which is difficult to promote to practical applications

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