Nano-Precision Motion Stage Learning Control for Noise-Robust Convergence
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Solution Overview
Problem
Existing ultra-precision motion stage control systems face limitations due to sensitivity to external noise and disturbances, which affect model estimation accuracy and convergence performance, and require complex computational methods with high-dimensional learning gains.
Innovation Solution
A learning control system for nano-precision motion stages is introduced, utilizing a closed-loop feedback section and a feedforward section with Fourier transformers, a learning controller, and an iteration backward shift operator to determine frequency domain feedforward signals, reducing noise impact and simplifying learning gain determination through adaptive mechanisms and differential methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model-based iterative learning control methods are used, then learning control can be implemented, but the system becomes sensitive to external random noise affecting model estimation accuracy
Solution Approach 1:
The patent extracts and separates the noise components from the model estimation process by using multiple excitation signals and averaging techniques. The external random noise is isolated and excluded from the final model parameters through statistical processing, thereby improving model estimation accuracy while reducing noise sensitivity.
Solution Approach 2:
The patent performs preliminary noise filtering and signal processing before model estimation. By pre-processing the measurement data to remove noise components and by designing excitation signals that minimize noise impact, the system achieves accurate model estimation without being sensitive to external random noise during the actual control operation.
2Measurement precision
If data-based iterative learning control methods are used, then model estimation can be performed, but external repetitive disturbances affect the accuracy
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors the output for repetitive disturbance patterns and adjusts the model estimation process accordingly. The identified disturbance patterns are fed back into the estimation algorithm to compensate for their effects, thereby maintaining accurate model estimation despite the presence of external repetitive disturbances.
Solution Approach 2:
The patent converts the harmful effect of external repetitive disturbances into useful information for improving model accuracy. By identifying and characterizing these disturbances, the system uses them to refine the model estimation process, ultimately achieving more accurate models that account for these previously harmful factors.
3Reliability
If iterative learning control with high-dimensional learning gains is used, then control performance can be improved, but computational complexity increases
Solution Approach 1:
The patent segments the high-dimensional learning gain matrix into smaller, manageable sub-matrices or blocks. This segmentation allows the control algorithm to process information in smaller chunks, reducing computational complexity while maintaining the overall control performance through the coordinated action of these segmented components.
Solution Approach 2:
The patent implements dynamic adjustment of learning gain dimensions based on the specific operational conditions and error characteristics. Rather than always using full high-dimensional learning gains, the system adaptively selects the appropriate dimensionality, reducing computational complexity when full-dimensional processing is not necessary while maintaining control performance when it is needed.
Data Source
AI summary
A learning control system for a nano-precision motion stage comprises a closed-loop feedback section including a motion trajectory generator, a feedback controller, a motion stage, and a first Fourier transformer; and a feedforward section including a second Fourier transformer, a learning controller, an iteration backward shift operator, and a Fourier inverse transformer. An iteration experiment count j is initialized as j=1, and a j-th frequency domain feedforward signal is initialized to 0; the system is run to collect a frequency domain error signal and a frequency domain position measurement signal; a (j+1)-th frequency domain feedforward signal is updated; and an iteration experiment count j is incremented by 1. The present disclosure can effectively suppress the influence of external noise and disturbances, and improve convergence performance. Moreover, the present disclosure requires less computation, achieves simple determination of learning gains and strong robustness, and is convenient for engineering applications.