Neural Network Motion Control With Filtered Real State Signals
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Solution Overview
Problem
Neural networks in motion control systems face challenges due to the reality gap between synthetic simulation signals and real machine signals, leading to malfunctions and suboptimal performance.
Innovation Solution
A computer-implemented method that filters real state signals using a filter unit, such as a low-pass filter, to reduce high-frequency jitter and bridge the reality gap, allowing neural networks trained on synthetic data to generate better control signals for machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If neural networks are trained on synthetic simulation data, then training efficiency and safety are improved, but the reality gap between synthetic and real signals causes malfunctions and reduced reliability
Solution Approach 1:
A filter unit is introduced as an intermediary component between the neural network and the real state signals. This filter processes the real signals before they reach the neural network, acting as a mediator that bridges the reality gap. The filter removes high-frequency jitter from real signals, making them more similar to synthetic training data, thereby enabling the neural network to process real signals reliably without requiring retraining.
Solution Approach 2:
The filter unit changes the parameters of real state signals by removing high-frequency components through filtering. This parameter transformation makes real signals more comparable to synthetic signals in terms of frequency characteristics, reducing the reality gap and improving the neural network's ability to process real data accurately.
2Measurement precision
If detailed sensor models are introduced to close the reality gap, then signal accuracy is improved, but the simulation signals only contain noise without useful new information
Solution Approach 1:
The filter unit extracts and removes only the harmful high-frequency jitter components from real state signals while preserving the useful low-frequency information. This selective extraction approach improves signal accuracy for neural network processing without introducing noise or losing valuable control information, unlike traditional detailed sensor models.
Data Source
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AI summary
A computer-implemented method for operating a control module (20) using a neural network (30) for controlling a machine (40) is proposed, comprising: receiving (S1) a number of real state signals (Z) indicating a current state of the machine, filtering (S2) the real state signals (Z) by means of a filter unit (12) to provide input data (ED) for the neural network (30) on the basis of the filtered state signals (G), and inputting (S3) the provided input data (ED) into the neural network (30) of the control module (20) to provide control signals (SD) for controlling the machine (40).