Abstract
We present a high-bandwidth, lightweight, and nonlinear output tracking technique for soft actuators that combines parsimonious recursive layers for forward output predictions and online optimization using Newton-Raphson. This technique allows for reduced model sizes and increased control loop frequencies when compared with conventional RNN models. Experimental results of this controller prototype on a single soft actuator with soft positional sensors indicate effective tracking of referenced spatial trajectories and rejection of mechanical and electromagnetic disturbances. These are evidenced by root mean squared path tracking errors (RMSE) of 1.8 mm using a fully connected (FC) substructure, 1.62 mm using a gated recurrent unit (GRU) and 2.11 mm using a long short term memory (LSTM) unit, all averaged over three tasks. Among these models, the highest flash memory requirement is 2.22 kB enabling co-location of controller and actuator.
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References
Chin, K., Hellebrekers, T., Majidi, C.: Machine learning for soft robotic sensing and control. Adv. Intell. Syst. 2, 1900171 (2020). https://doi.org/10.1002/aisy.201900171
Hyatt, P., Wingate, D., Killpack, M.D.: Model-based control of soft actuators using learned non-linear discrete-time models. Frontiers Robot. AI 6, 2296–9144 (2019). https://doi.org/10.3389/frobt.2019.00022
Gillespie, M.T., Best, C.M., Townsend, E.C., Wingate, D., Killpack, M.D.: Learning nonlinear dynamic models of soft robots for model predictive control with neural networks. In: 2018 IEEE International Conference on Soft Robotics (RoboSoft), Livorno, pp. 39–45 (2018). https://doi.org/10.1109/ROBOSOFT.2018.8404894.
Bruder, D., Gillespie, B., Remy, C.D., Vasudevan, R.: Modeling and control of soft robots using the koopman operator and model predictive control. arXiv preprint arXiv:1902.02827 (2019)
Giorelli, M., Renda, F., Calisti, M., Arienti, A., Ferri, G., Laschi, C.: Neural network and Jacobian method for solving the inverse statics of a cable-driven soft arm with nonconstant curvature. IEEE Trans. Robot. 31(4), 823–834 (2015). https://doi.org/10.1109/TRO.2015.2428511
Xu, P.A., Mishra, A.K., Bai, H., Aubin, C.A., Zullo, L., Shepherd, R.F.: Optical lace for synthetic afferent neural networks. Sci. Robot. 4(34), eaaw6304 (2019)
Soloway, D., Haley, J.P.: Neural generalized predictive control. In: Proceedings of the 1996 IEEE International Symposium on Intelligent Control (1996)
Thuruthel, T.G., Shih, B., Laschi, C., Tolley, M.T.: Soft robot perception using embedded soft sensors and recurrent neural networks. Sci. Robot. 4(26), eaav1488 (2019)
Hughes, D., Heckman, C., Correll, N.: Materials that make robots smart. Int. J. Robot. Res. 38(12–13), 1338–1351 (2019)
Aguasvivas Manzano, S., Hughes, D.T., Simpson, C.R., Patel, R., Correll, N.: Embedded neural networks for robot autonomy. In: 2019 International Symposium on Robotics Research (ISRR), Hanoi (2019)
Grant, M., Boyd, S.: CVX: Matlab software for disciplined convex programming. version 2.0 beta. http://cvxr.com/cvx September 2013
Huber, P.J.: Robust estimation of a location parameter. Ann. Stat. 53(1), 73–101 (1964). https://doi.org/10.1214/aoms/1177703732
Acknowledgement
We would like to thank Dr. Don Soloway, Prof. Bradley Hayes and CAIRO Lab at CU Boulder and Cooper Simpson. This research has been supported by the Air Force Office of Scientific Research (Grant No. 83875–11094), we are grateful for this support.
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Manzano, S.A., Xu, P., Ly, K., Shepherd, R., Correll, N. (2021). High-Bandwidth Nonlinear Control for Soft Actuators with Recursive Network Models. In: Siciliano, B., Laschi, C., Khatib, O. (eds) Experimental Robotics. ISER 2020. Springer Proceedings in Advanced Robotics, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-030-71151-1_52
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DOI: https://doi.org/10.1007/978-3-030-71151-1_52
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