Indenter Load Control Using Fuzzy Prediction for Stable Force Tracking
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Solution Overview
Problem
The challenge in micro-nano indentation testing is to design a stable and accurate load control method for an indenter that is superior in control performance and simple to implement, as existing methods face difficulties in establishing an accurate physical model due to variations in load-displacement curves and sensor inaccuracies influenced by material properties and environmental factors.
Innovation Solution
A load control method based on fuzzy predictive control is introduced, which involves acquiring actual and expected force values, calculating errors and error change rates, optimizing a fuzzy predictive controller, and adjusting motor movement steps to achieve precise load control during loading, full load, and unloading stages, ensuring robustness and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional physical models are used for load control, then the control method is simple to implement, but the measurement precision and control accuracy deteriorate due to sensor inaccuracies and environmental factors
Solution Approach 1:
The patent implements a feedback mechanism where the actual measured force value is continuously compared with the expected force value, and the error is used to adjust the motor movement steps in real-time. This closed-loop feedback system compensates for sensor inaccuracies and environmental variations, improving load control accuracy without significantly increasing implementation complexity
Solution Approach 2:
The patent replaces traditional mechanical physical models with a fuzzy predictive control algorithm that uses computational methods to determine motor movement steps. This substitution allows the system to adapt to varying conditions through software-based control rather than relying on fixed mechanical parameters, thereby improving measurement precision
2Measurement precision
If fuzzy predictive control is implemented to improve control accuracy, then the load control precision improves, but the device complexity increases due to the algorithm implementation
Solution Approach 1:
The fuzzy predictive control algorithm is designed to be self-adjusting, where the system automatically determines the appropriate motor movement steps based on the error between actual and expected force values. The controller adapts to different loading stages (loading, full load, unloading) without requiring external intervention or complex configuration, thereby limiting the increase in device complexity
Solution Approach 2:
The system dynamically changes control parameters (motor movement steps) based on the current loading stage and error conditions. By adjusting parameters adaptively rather than using fixed values, the system achieves high control accuracy while maintaining relatively simple device architecture through software-based parameter management
3Stability of the object's composition
If the control system continuously adjusts motor steps to reduce error, then the stability of load control improves, but the loss of time increases due to multiple adjustment cycles
Solution Approach 1:
The fuzzy predictive control algorithm predicts the required motor movement steps in advance based on the current error and error change rate. By calculating the optimal adjustment step beforehand rather than making incremental adjustments, the system achieves stable load control with fewer control cycles, thereby reducing the time loss associated with continuous adjustments
Data Source
AI summary
A load control method and a load control system of an indenter based on fuzzy predictive control, are provided. The method includes acquiring an actual measured force value of a sensor and an expected force value of the nth cycle in the loading stage; calculating a first error and a change rate; establishing and optimizing a fuzzy predictive controller; determining movement steps of a motor in the loading stage; acquiring the actual measured value of the sensor and an expected force value of the nth cycle in the full load stage; controlling the movement of the motor; acquiring the actual measured force value of the sensor and an expected force value of the nth cycle in the unloading stage; calculating a third error and a change rate; and determining the movement steps of the motor in the unloading stage.