MPC Model Update Using Engineering Constraints
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Solution Overview
Problem
Model Predictive Control (MPC) systems face challenges in maintaining accurate control due to inaccuracies in the inverse gain matrix, leading to poor control performance and system instability, especially when engineering judgment is not adequately incorporated into the model updates.
Innovation Solution
A method and apparatus for updating the MPC controller's dynamic model by incorporating user-defined constraints, such as steady-state gain constraints and RGA uncertainty, to ensure that the inverse gain matrix accurately reflects engineering judgment, thereby maintaining control actions in the correct direction and magnitude.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If model updates are performed using standard identification methods, then the model can adapt to new process conditions, but the inverse gain matrix accuracy deteriorates and control actions may move in wrong directions
Solution Approach 1:
The patent applies preliminary action by pre-defining engineering judgment constraints (steady-state gain constraints, RGA uncertainty constraints, collinearity constraints) before model identification. These constraints are incorporated into the identification process to ensure that model updates maintain inverse gain matrix accuracy while adapting to new conditions, preventing control actions from moving in wrong directions
Solution Approach 2:
The patent changes parameters by modifying the model identification process to include engineering judgment constraints. Specifically, it adjusts the identification algorithm to satisfy steady-state gain constraints, RGA uncertainty constraints, and collinearity constraints, thereby maintaining reliability while achieving adaptability
2Measurement precision
If engineering judgment constraints are incorporated into model updates, then control action accuracy is improved, but the model identification complexity increases
Solution Approach 1:
The patent segments the model identification process into distinct constraint categories: steady-state gain constraints, RGA uncertainty constraints, and collinearity constraints. This segmentation allows each type of engineering judgment to be systematically incorporated, improving control action accuracy while managing identification complexity through structured organization
Solution Approach 2:
The patent implements feedback by using the identified model to calculate the inverse gain matrix and verifying it against engineering judgment constraints. If constraints are violated, the identification process is adjusted iteratively until the model satisfies all constraints, ensuring control action accuracy
Data Source
AI summary
A system and method of model predictive control executes a model predictive control (MPC) controller of a subject dynamic process (e.g., processing plant) in a configuration mode, identification mode and model adaptation mode. Users input and specify model structure information in the configuration mode, including constraints. Using the specified model structure information in the identification mode, the MPC controller generates linear dynamic models of the subject process. The generated linear dynamic models collectively form a working master model. In model adaptation mode, the MPC controller uses the specified model structure information in a manner that forces control actions based on the formed working master model to closely match real-world behavior of the subject dynamic process. The MPC controller coordinates execution in identification mode and in model adaptation mode to provide adaptive modeling and preserve structural information of the model during a model update.