PID Control Parameter Computation for Stable Tuning Without Overshoot
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Solution Overview
Problem
Existing methods for determining control parameters in PID control require trial and error, leading to increased workload and instability issues such as overshoot or undershoot in control systems.
Innovation Solution
A control parameter computation method that identifies multiple mathematical models for each time segment using system identification, computes adaptiveness, and optimizes parameter Lambda to ensure stability and prevent overshoot or undershoot, reducing the need for trial and error by employing a stability function builder and parameter Lambda computation section.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If trial and error method is used to decide mathematical model and parameter Lambda, then control parameters can be obtained, but workload increases and stability issues occur
Solution Approach 1:
The patent performs preliminary system identification to obtain multiple mathematical models before control parameter determination. By pre-processing the system characterization and organizing models in advance, the method eliminates the need for trial-and-error during actual control setup, directly providing stable control parameters through the stability function evaluation.
Solution Approach 2:
The patent replaces the manual trial-and-error mechanical adjustment process with an automated computational system. The stability function builder and parameter computation section automatically evaluate multiple mathematical models and determine optimal control parameters through algorithmic processing, substituting human iterative adjustment with systematic computational analysis.
2Measurement precision
If multiple mathematical models are identified for each time segment, then control parameter accuracy improves, but computation complexity increases
Solution Approach 1:
The patent segments the system identification process into distinct time segments, identifying multiple mathematical models for each segment. This segmentation allows the complex computation to be distributed across manageable segments, where each segment's models can be independently evaluated and combined, reducing overall computational complexity while maintaining high accuracy.
Solution Approach 2:
The patent changes the parameter representation by introducing a stability function that evaluates multiple mathematical models across different time segments. By transforming the problem from selecting one complex model to evaluating multiple simpler models through a unified stability criterion, the method achieves high accuracy without proportional increases in computational complexity.
Data Source
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AI summary
A control parameter computation method includes, by a processor, identifying, for each time segment, a plurality of mathematical models of different structures. The method also includes, computing an adaptiveness representing a level of adaptation between a time series of the computed control variable predicted values and time series data of the control variable in the paired data corresponding to the time segments different from the time segment employed in the identification. The method also includes, selecting, as a mathematical model for the paired data of the time segment. The method also includes, configuring a PID controller, generating a transfer function represented by a product of the PID controller and the selected mathematical model, and generating a function representing a gain margin of the transfer function and a function representing a phase margin of the transfer function. The method also includes computing a parameter Lambda.