Closed-Loop Parameter Tuning Using Disturbance Models and Feedback
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Solution Overview
Problem
Existing control systems face challenges in systematically accounting for disturbances introduced by non-ideal sensors and network variability, leading to suboptimal controller parameter settings in complex control loops.
Innovation Solution
A method to determine operational parameters of a closed control loop components by modeling non-controllable disturbances and optimizing controller and sensor settings using simulation and machine learning, allowing for continuous adaptation to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If empirical methods are used to determine controller parameters, then the implementation is simple, but the control loop quality is suboptimal because sensor disturbances and network variability are not systematically accounted for
Solution Approach 1:
The patent applies preliminary action by determining model parameters of disturbance factors before optimizing controller parameters. The method first characterizes sensor disturbances and network variability through systematic measurements and modeling, then uses these pre-determined disturbance models to guide the optimization of controller parameters, ensuring that the optimization process accounts for actual system disturbances rather than relying on empirical trial-and-error
Solution Approach 2:
The patent implements feedback by using a quality measure that systematically evaluates control loop performance based on the determined disturbance models. This quality measure provides feedback during the parameter optimization process, allowing iterative improvement of controller parameters until the control loop quality is maximized, thereby resolving the contradiction between simple empirical tuning and optimal control performance
2Reliability
If controller parameters are optimized for ideal conditions, then theoretical performance is maximized, but actual performance degrades due to unmodeled disturbances from non-ideal sensors and network variability
Solution Approach 1:
The patent uses feedback by implementing a quality measure that systematically evaluates control loop performance while accounting for disturbance characteristics. This quality measure provides continuous feedback during optimization, allowing the system to adjust parameters based on actual disturbance effects rather than theoretical ideal conditions, thereby improving actual control performance
Solution Approach 2:
The patent applies self-service by enabling the control system to automatically characterize its own disturbances through systematic measurements and modeling. The system performs self-diagnosis of sensor and network disturbances, then uses this self-knowledge to optimize its own parameters, eliminating the need for external expert intervention and improving adaptability to actual operating conditions
3Adaptability or versatility
If fixed controller parameters are used, then the implementation is straightforward, but the system cannot adapt to changing disturbance conditions such as network congestion or sensor aging
Solution Approach 1:
The patent applies dynamics by transitioning from fixed controller parameters to dynamic, adaptive parameters. The system continuously monitors disturbance characteristics and automatically adjusts controller parameters in response to changing conditions such as network congestion or sensor aging. This dynamic adaptation maintains optimal control performance without requiring manual reconfiguration, resolving the contradiction between ease of operation and adaptability
Solution Approach 2:
The patent implements feedback through a quality measure that evaluates control loop performance under varying disturbance conditions. This feedback mechanism enables the system to detect changes in disturbance characteristics and trigger parameter re-optimization automatically, providing continuous adaptation to changing conditions while maintaining straightforward operation through automated processes
Data Source
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AI summary
A Method of Determining at least one operational parameter of a component of a closed control loop, the closed control loop comprising a controller for receiving an error signal and producing a control signal, a system to be controlled by means of the control signal and producing an output, a sensor for measuring the output and producing a measured output signal, comprising the steps: Determining model parameters of a model of at least one non-controllable disturbance factor of the closed control loop; Determining a quality measure of the closed control loop and Determining, from the model, at least one controllable parameter of the controller, the system and/or the sensor such that the quality measure is improved.