تقرير
High-bandwidth nonlinear control for soft actuators with recursive network models
| العنوان: | High-bandwidth nonlinear control for soft actuators with recursive network models |
|---|---|
| المؤلفون: | Manzano, Sarah Aguasvivas, Xu, Patricia, Ly, Khoi, Shepherd, Robert, Correll, Nikolaus |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science Mathematics |
| مصطلحات موضوعية: | Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Software Engineering, Electrical Engineering and Systems Science - Systems and Control, Mathematics - Numerical Analysis |
| الوصف: | 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 1.8mm using a fully connected (FC) substructure, 1.62mm using a gated recurrent unit (GRU) and 2.11mm using a long short term memory (LSTM) unit, all averaged over three tasks. Among these models, the highest flash memory requirement is 2.22kB enabling co-location of controller and actuator. Comment: International Symposium on Experimental Robotics (ISER) 2020, Malta |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1007/978-3-030-71151-1_52 |
| URL الوصول: | http://arxiv.org/abs/2101.01139 |
| رقم الأكسشن: | edsarx.2101.01139 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1007/978-3-030-71151-1_52 |
|---|