Hysteresis Compensation in Force/Torque Sensors Using Time Series Information
Abstract
:1. Introduction
2. Basic Principles
2.1. Signals of a Strain Gauge-Type Force Sensor
2.2. Linear Regression
3. Proposed Method
3.1. Machine Learning Incorporating Time Series Information
3.1.1. Acquiring Time Series Information
3.1.2. Utilization of Time Series Information for Machine Learning
3.2. NN and Hybrid Model
4. Experiment
4.1. Experimental Equipment
4.1.1. Strain Gauge and Strain Measuring Device
4.1.2. Robot Arm-Based Force Application Device
4.2. Learning
4.2.1. NN Structure and the Hybrid Model
4.2.2. Training Patterns
- Pattern A
- (amount of training data: large)Six different paths are given in the experiments, so that most of the operating range is covered. The trajectories are all based on sinusoidal waves with random amplitude in each direction. The frequency of the sinusoidal wave was set to 0.05 Hz to avoid error owing to inertia force error. Five of these were used for training and the other was used for testing. Each path was examined with three trajectories with different velocities. Depending on the speed of each trajectory, the recorded time differs as 95 s, 135 s, and 175 s for fast, medium and slow speeds, respectively. We acquired five paths for each of these, recorded them at a rate of 20 samples/s and performed training with a total of 202,500 samples.
- Pattern B
- (amount of training data: small)The amount of data used for the training and testing of pattern A was reduced to 1/10th of the original size, simply by decimating the same trajectory. Through this operation, we created a state in which the amount of data was simply reduced and verified the precision of each technique. Depending on the speed of each trajectory, the time recorded differs as 95 s, 135 s and 175 s for fast, medium and slow speeds. We acquired five paths for each of these, recorded them at a rate of 2 samples/s and performed training with a total of 20,250 samples.
4.3. Results
4.3.1. Pattern A (Amount of Training Data: Large)
4.3.2. Pattern B (Amount of Training Data: Small)
4.3.3. Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
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Koike, R.; Sakaino, S.; Tsuji, T. Hysteresis Compensation in Force/Torque Sensors Using Time Series Information. Sensors 2019, 19, 4259. https://doi.org/10.3390/s19194259
Koike R, Sakaino S, Tsuji T. Hysteresis Compensation in Force/Torque Sensors Using Time Series Information. Sensors. 2019; 19(19):4259. https://doi.org/10.3390/s19194259
Chicago/Turabian StyleKoike, Ryuichiro, Sho Sakaino, and Toshiaki Tsuji. 2019. "Hysteresis Compensation in Force/Torque Sensors Using Time Series Information" Sensors 19, no. 19: 4259. https://doi.org/10.3390/s19194259
APA StyleKoike, R., Sakaino, S., & Tsuji, T. (2019). Hysteresis Compensation in Force/Torque Sensors Using Time Series Information. Sensors, 19(19), 4259. https://doi.org/10.3390/s19194259