Application of Iterative Machine Learning for Output Tracking With Magnetic Soft Actuators

This paper presents experimental results of output tracking for soft magnetic actuators using iterative machine learning (IML) to estimate the model and its uncertainty. A challenge is that parameters of dynamic-response models of such soft actuators are sensitive to the operating point. To overcome this challenge and improve the tracking performance, a complex-valued Gaussian process regression (CGPR) is used to estimate a system inverse model; additionally, the CGPR approach provides an uncertainty measure, which is used to design the frequency-dependent iteration gain to ensure convergence. This approach leads to convergence even when data near resonance (where models tend to be sensitive) is not available. Moreover, a method to improve the model estimation is proposed, in which the initial training signal is augmented with additional frequency content to increase the input and output signal size compared to the noise. Using this augmented approach, the IML approach results in a 95% reduction in the maximum tracking error compared to using the dc gain alone. The final maximum tracking error is 1.8%, which is near the sensor resolution limit.