Model-Based Predictive Controller Parameterization Tool
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Solution Overview
Problem
The existing engineering tools for parameterizing model-based predictive controllers require control-oriented know-how and often involve time-consuming and costly 'trial and error' experiments to determine weighting factors, which can lead to instability and performance losses due to model uncertainties.
Innovation Solution
An engineering tool that automatically calculates meaningful weighting factors using an analytical approach, based on manipulated variable step changes and model simulations, to improve the robustness and usability of model-based predictive controllers, allowing for reduced manipulation intervention and increased stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual parameterization with trial and error experiments is used to determine weighting factors, then control performance can be optimized, but time consumption and costs increase significantly
Solution Approach 1:
The system performs self-parameterization by automatically determining weighting factors through model analysis and calculation, eliminating the need for manual trial-and-error experiments. The controller autonomously optimizes its parameters based on process model characteristics, achieving both time efficiency and control performance.
Solution Approach 2:
The invention changes the approach from manual parameter tuning to automated parameter calculation based on model characteristics. By deriving weighting factors from process model parameters and performance specifications, the system transforms the parameterization process into a systematic calculation rather than iterative experimentation.
2Reliability
If manual parameterization is used, then control-oriented know-how can be applied, but the complexity of the parameterization process increases
Solution Approach 1:
The controller performs self-parameterization by automatically determining weighting factors through model analysis, eliminating the need for operators to possess specialized control tuning knowledge. The system encapsulates the complexity within automated algorithms while presenting a simplified interface to users.
Solution Approach 2:
The invention replaces manual mechanical tuning processes with automated computational methods. Instead of relying on operator experience and iterative manual adjustment, the system uses algorithmic calculation based on process models to determine optimal parameters, substituting human expertise with systematic computation.
Data Source
AI summary
An engineering tool and a method for parameterizing a model-based predictive controller for controlling a process-engineering process. A quality determining module for determining the quality of the model, which contains the model-based predictive controller for the behavior of the process-engineering process that is to be controlled, uses measurement data to ascertain errors in various model parameters as model errors. The weighting factors used to weight manipulated variable changes in a quality criterion are determined by a weighting factor module such that manipulated variable changes of a model-based predictive controller designed taking into account model errors are the same as those of a predictive controller designed based on the assumption of an error-free model. This advantageously results in good performance with simultaneously adequate stability of the controller. In addition, the parameterization of the controller which the user needs to perform requires no control-engineering know-how or the performance of complex trials.