Online Tuning Optimization for Nonlinear Process Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing control systems rely heavily on heuristic methods for determining tuning parameters, which are often offline and require significant expertise, limiting their applicability and efficiency in real-time process control, especially for nonlinear processes.
Innovation Solution
The implementation of a control system that includes state transition optimization circuitry, control optimization circuitry, and tuning optimization circuitry, which uses a parametric hybrid model to determine tuning parameters systematically based on a closed-form solution to an augmented objective function, allowing for online tuning and reduced reliance on user expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If heuristic methods are used for determining tuning parameters, then the control system can be implemented with existing methods, but the tuning process requires significant expertise and is limited in real-time applicability
Solution Approach 1:
The control system performs self-tuning by automatically determining optimal tuning parameters through the tuning optimization module, which solves the augmented objective function without requiring external expert intervention. The system uses its own process model and performance data to autonomously adjust tuning parameters in real-time, eliminating dependence on user expertise while maintaining real-time applicability.
Solution Approach 2:
The system dynamically changes tuning parameters by solving an augmented objective function that explicitly optimizes these parameters. The tuning optimization module computes optimal values for weighting factors and other tuning parameters based on current process conditions, allowing the system to adapt parameters in real-time rather than relying on fixed heuristic values or expert-tuned settings.
2Adaptability or versatility
If heuristic methods are used for tuning parameters, then implementation is simpler, but the method lacks systematic approach for nonlinear processes
Solution Approach 1:
The patent introduces a tuning optimization module as an intermediary component that bridges the control optimization and process control. This module systematically handles the complexity by formulating and solving an augmented objective function that incorporates both control performance and tuning parameter optimization, providing a structured approach for nonlinear processes without making the overall system unnecessarily complex.
Solution Approach 2:
The control system is segmented into distinct functional modules: process model, control optimization, and tuning optimization. This segmentation allows each module to specialize in specific tasks - the tuning optimization module focuses exclusively on determining optimal tuning parameters through systematic optimization of the augmented objective function, while other modules handle their respective functions, making the overall system more manageable and adaptable to nonlinear processes.
3Productivity
If online tuning is implemented, then the control system adapts to changing conditions, but computational complexity increases
Solution Approach 1:
The system performs preliminary formulation of the augmented objective function that combines control performance metrics with tuning parameter optimization. By pre-structuring the optimization problem in this augmented form, the system enables efficient online solution through dedicated tuning optimization algorithms, reducing the computational burden during real-time operation while maintaining the ability to adapt to changing conditions.
Data Source
AI summary
One embodiment of the present disclosure describes an industrial system, which includes a control system that controls operation of an industrial process by instructing an automation component in the industrial system to implement a manipulated variable setpoint. The control system includes a process model that model operation of the industrial process, control optimization that determines the manipulated variable setpoint based at least in part on the process model, a control objective function, and constraints on the industrial process, in which the control objective function includes a tuning parameter that describes weighting between aspects of the industrial process affected by the manipulated variable setpoint; and tuning optimization circuitry that determines the tuning parameter based at least in part on a tuning objective function, in which the tuning objective function is determined based at least in part on a closed form solution to an augmented version of the control objective function, which includes the constraints as soft constraints.