Model Predictive Controller Updating via White Noise Diagnostics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Model Plant Mismatch (MPM) leads to inaccurate predictions and performance degradation in Model Predictive Control (MPC), requiring lengthy and expertise-dependent model re-identification processes that result in production of low-quality products during perturbation periods.
Innovation Solution
A method and system for updating a model predictive controller by assessing performance deviations, diagnosing MPM through white noise addition to set points, calculating model prediction errors, and quantifying mismatch using correlation coefficients, thereby reducing the perturbation period and efficiently updating the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional model re-identification is performed to diagnose MPM, then model accuracy is improved, but perturbation period increases and productivity decreases
Solution Approach 1:
The patent changes the parameters of the perturbation signal by using optimized white noise characteristics and adjusting the excitation level to achieve sufficient model identification accuracy with minimal perturbation duration, thereby reducing off-spec product generation
Solution Approach 2:
The system performs preliminary diagnostics using process data before initiating full model re-identification, allowing early detection of MPM conditions and preparation of updated models in advance to minimize perturbation periods
2Measurement precision
If traditional model re-identification is performed to diagnose MPM, then model accuracy is improved, but time consumption increases
Solution Approach 1:
The patent implements periodic model updating at predetermined time intervals or when performance degradation thresholds are reached, allowing the system to maintain acceptable model accuracy without continuous perturbation, thus reducing total perturbation time
Solution Approach 2:
The system applies partial model updating by re-identifying only specific model parameters or subsystems that have degraded rather than performing complete model re-identification, significantly reducing the perturbation period required
3Ease of operation
If automated model updating is implemented, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The system performs self-diagnosis and self-updating of models by automatically detecting MPM conditions, selecting appropriate identification methods, and implementing model updates without operator intervention, improving ease of operation while managing complexity through automation
Data Source
Figure 1
Figure 2
AI summary
The invention relate to a method for updating a model in a model predictive controller. The method comprises assessing the deviation of the operating performance level from the desired performance level of the process plant. Diagnosing the model predictive control for the model plant mismatch by updating the model in a model predictive controller. Diagnosing the model predictive controller comprises determining the model prediction error in relation to model plant mismatch, quantifying the model plant mismatch, and updating the model in the said model predictive controller. The invention also relate to a system for updating a model in a model predictive controller in accordance with the method of the invention.