Regulatory Controller Tuning Using Positive-Change Policy Updates
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Solution Overview
Problem
Existing regulatory controllers are often poorly tuned, leading to inefficient operation, wasted energy, and excessive wear on control system components. Manual tuning is tedious, error-prone, and time-consuming, especially in systems with multiple controllers.
Innovation Solution
An automated method for tuning regulatory controllers, which updates controller policies iteratively based on performance levels. The updated policies are optimized to have the highest likelihood of producing a positive change in performance rather than maximizing the largest positive magnitude of change.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tuning of regulatory controllers is performed, then tuning accuracy can be improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs self-tuning by automatically adjusting controller parameters based on observed process behavior and performance feedback. The regulatory controller monitors its own performance metrics and autonomously modifies tuning parameters without requiring manual intervention, thereby achieving high tuning accuracy while eliminating time-consuming manual operations.
Solution Approach 2:
The system implements continuous feedback loops where performance data from the controlled process is fed back to the tuning mechanism. This feedback drives iterative adjustments of controller parameters, enabling the system to learn from past performance and converge toward optimal tuning settings automatically, resolving the contradiction between accuracy and time consumption.
2Productivity
If aggressive tuning adjustments are made to maximize performance improvement, then productivity can be improved, but system stability and reliability deteriorate
Solution Approach 1:
The system applies partial adjustments to controller parameters rather than extreme changes. By making conservative, incremental modifications to tuning parameters based on performance feedback, the system achieves steady performance improvement while maintaining system stability and avoiding disruptive changes that would compromise reliability.
Solution Approach 2:
The tuning mechanism dynamically adapts its adjustment strategy based on current system conditions and performance trends. The system modulates the aggressiveness of parameter changes in real-time, applying larger adjustments when stability margins are high and smaller adjustments when the system is near stability limits, thereby balancing productivity improvement with reliability maintenance.
Data Source
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AI summary
During each of a plurality of iterations, a policy of a controller is updated and at least part of a process is controlled using the updated policy. The updated policy is associated with a performance level of the controller. For each iteration, the updated policy is determined using the associations generated during one or more previous iterations between the policies and the corresponding performance levels of the controller in controlling the at least part of the process, such that the updated policy is optimized to have a highest likelihood of producing a positive change in the performance level of the controller in controlling the at least part of the process rather than optimized to have a highest likelihood of producing a largest positive magnitude of change in the performance level of the controller in controlling the at least part of the process relative to the previous iteration.