Dual-Mode Model Predictive Control Under Scan Time Constraints
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Solution Overview
Problem
Model-based predictive control in process control systems faces limitations due to the need for accurate process models, which can change over time, requiring frequent regeneration and interrupting normal process operation, leading to inefficiencies and reduced adoption in the industry.
Innovation Solution
Implementing a dual-mode operation for model-based controllers that can switch between constrained and unconstrained solution modes, allowing for superior control by using constrained solutions when possible and unconstrained solutions when time constraints are violated, enabling continuous process control without interrupting normal operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If model-based predictive control is implemented, then control performance is improved, but frequent model regeneration is required which interrupts normal process operation
Solution Approach 1:
The system performs preliminary actions by generating multiple candidate move plans (constrained and unconstrained) in advance during the scan period. These candidate plans are prepared beforehand so that when the scan period ends, the controller can immediately select and implement the best available plan without interruption, thus maintaining both control performance and operational continuity.
Solution Approach 2:
The system dynamically adapts its control strategy by switching between constrained and unconstrained solution modes based on real-time conditions. The controller generates constrained move plans when process constraints are satisfied, and switches to unconstrained move plans when constraints cannot be met, allowing the system to maintain optimal control performance while avoiding interruptions from frequent model regeneration.
2Measurement precision
If constrained solutions are used, then control accuracy is improved, but computational time increases which may violate scan period constraints
Solution Approach 1:
The system applies partial action by generating constrained move plans to the extent possible within the scan period. If constrained optimization cannot be completed in time, the controller partially uses the available computational results or switches to pre-generated unconstrained plans, ensuring that control decisions are always made without violating scan period constraints while maintaining as much accuracy as possible.
Solution Approach 2:
The control solution is segmented into two distinct modes: constrained move plans for when constraints can be satisfied, and unconstrained move plans for when they cannot. This segmentation allows the system to efficiently manage computational resources by selecting the appropriate level of optimization based on real-time conditions, balancing control accuracy with computational time requirements.
Data Source
AI summary
The disclosed systems and techniques enable dual mode operation for model-based controllers in which the controllers are capable of operating in both (i) a constrained solution mode, and (ii) an unconstrained solution mode. The dual mode operation improves control because it enables the use of constrained solution mode operation when possible (constrained solution mode often enables superior control) and enables the use of unconstrained solution mode when constrained solution mode is not possible (e.g., when it is impossible to develop the constrained solution with the time available). This enables superior control when compared to typical model predictive control (MPC) controllers.