Model Predictive Controller Using Segmented Linearized Models
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Solution Overview
Problem
Conventional process control systems face high computational loads when linearizing processes with complex non-linear dynamics, leading to inefficiencies in control performance and increased computational time.
Innovation Solution
A method and system that provide a non-linear model and generate multiple linearized models at different rates, allowing for selection based on a reference model to optimize control signals and reduce computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If linearization is performed at all steps of the process to obtain a conventional MPC solution, then control performance is improved, but computational load greatly increases
Solution Approach 1:
The process is divided into multiple operating regions, and a single linearized model is segmented to represent different regions. The controller selects appropriate segments based on current operating conditions, avoiding the need to perform linearization at every step while maintaining control performance across the entire operating range.
Solution Approach 2:
The system dynamically switches between different linearized model segments based on real-time operating conditions. This dynamic selection allows the controller to adapt to changing process conditions without repeatedly performing computationally intensive linearization operations, thus reducing computational load while maintaining control performance.
2Device complexity
If a single linearized model is used to represent the entire operating range, then computational load is reduced, but control performance deteriorates due to inability to capture non-linear dynamics
Solution Approach 1:
The operating range is segmented into multiple regions, and the single linearized model is segmented to represent different operating conditions. By selecting appropriate model segments based on current operating conditions, the system captures non-linear dynamics across the entire range while using only one linearized model, thus maintaining low computational load.
Solution Approach 2:
The system changes parameters (model segment selection) based on operating conditions rather than changing the model structure itself. This allows the controller to adapt to non-linear dynamics by selecting different parameter sets from the single linearized model, maintaining control performance without increasing computational complexity.
Data Source
AI summary
A method and system for process control using a model predictive controller. The control system can have one or more control devices operably coupled to a processing system for controlling a process of the processing system; a modeling tool to provide a non-linear model based at least in part on the process and to provide a plurality of linearized models based at least in part on the non-linear model, where the plurality of linearized models are linearized at different linearization rates; and a controller operably coupled to the modeling tool. The controller can select one of the plurality of linearized models based on a comparison of the plurality of linearized models with a reference model. The controller can send one or more control signals to at least one of the one or more control devices. The one or more control signals can be determined using the selected one of the plurality of linearized models.