Adaptive Sampling Rate Control for Plant Response Time Tuning
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Solution Overview
Problem
Determining an appropriate sampling rate for feedback controllers is challenging, as too fast a rate can lead to improper tuning of proportional gain, while too slow a rate can impair performance, especially in dynamic systems affected by external conditions.
Innovation Solution
A control system that estimates a plant's response time and adjusts the sampling rate based on this parameter, using a controller configured to detect disturbances and evaluate signals affected by them, allowing for adaptive sampling rate adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the sampling rate is increased to improve controller performance, then the responsiveness and control accuracy improve, but the proportional gain becomes too small and tuning becomes improper
Solution Approach 1:
The patent applies dynamics by making the sampling rate adaptive rather than fixed. The controller automatically adjusts the sampling rate based on real-time system conditions, plant response time estimates, and disturbance characteristics. This dynamic adjustment allows the system to optimize performance during transient conditions while maintaining proper tuning during steady-state operation, resolving the contradiction between responsiveness and tuning accuracy.
Solution Approach 2:
The patent changes the sampling rate parameter based on estimated plant response time and disturbance detection. By varying this critical parameter according to system state, the controller achieves high responsiveness when needed (fast sampling during transients) while maintaining proper tuning (slower sampling during steady-state), thus resolving the technical contradiction.
2Reliability
If the sampling rate is decreased to improve tuning accuracy, then the proportional gain tuning becomes more accurate, but the controller performance deteriorates
Solution Approach 1:
The adaptive sampling rate mechanism dynamically switches between fast and slow sampling rates based on system conditions. During steady-state operation, a slower sampling rate is used to maintain tuning accuracy, while during transient conditions or disturbances, the sampling rate increases to maintain performance. This temporal separation of sampling rates resolves the contradiction.
Solution Approach 2:
The controller periodically re-evaluates system conditions and adjusts the sampling rate accordingly. This periodic adaptation allows the system to maintain accurate tuning during normal operation while rapidly responding to changing conditions, effectively managing the trade-off between tuning accuracy and performance.
3Productivity
If adaptive tuning algorithms are used to automatically adjust control parameters, then the performance improves relative to fixed tuning, but the complexity of determining appropriate sampling rate increases
Solution Approach 1:
The adaptive tuning algorithm serves itself by automatically determining the appropriate sampling rate based on plant response time estimates and disturbance detection. The system self-adjusts without external intervention, using its own measurements and models to set the sampling rate. This self-service capability maintains high performance while managing complexity through autonomous operation.
Solution Approach 2:
The system uses feedback from plant response time measurements and disturbance detection to automatically adjust the sampling rate. This closed-loop approach allows the controller to adapt to changing conditions and maintain optimal performance without manual intervention, resolving the complexity issue through intelligent feedback-based automation.
4Device complexity
If manual tuning intervention is used to set control parameters, then the sampling rate can be fixed and simple, but the performance suffers in dynamic systems with external disturbances
Solution Approach 1:
The system replaces manual tuning with automatic adaptive tuning that self-adjusts control parameters and sampling rate based on real-time conditions. This eliminates the need for continuous manual intervention while significantly improving performance in dynamic systems, resolving the contradiction between simplicity and performance.
Solution Approach 2:
The patent introduces dynamic adaptation to the previously static manually-tuned system. By making the sampling rate and control parameters adaptive rather than fixed, the system maintains simplicity of operation (no manual tuning needed) while achieving superior performance in dynamic conditions through automatic adjustment.
Data Source
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AI summary
A control system (200) for a plant (204) includes a controller (210) and a sensor. The controller (210) is configured to estimate a response time of the plant (204) and adjust a sampling rate based on the estimated response time. The response time is a parameter that characterizes a response of the plant (204) to a disturbance. The sensor is configured to receive the adjusted sampling rate from the controller (210), collect samples of a measured variable from the plant (204) at the adjusted sampling rate, and provide the samples of the measured variable to the controller (210).